U.S. patent number 8,703,424 [Application Number 13/069,326] was granted by the patent office on 2014-04-22 for predicting human developmental toxicity of pharmaceuticals using human stem-like cells and metabolomics.
This patent grant is currently assigned to Stemina Biomarker Discovery, Inc.. The grantee listed for this patent is Gabriela G. Cezar, Elizabeth L. R. Donley, Alan M. Smith, April M. Weir-Hauptman, Paul R. West. Invention is credited to Gabriela G. Cezar, Elizabeth L. R. Donley, Alan M. Smith, April M. Weir-Hauptman, Paul R. West.
United States Patent |
8,703,424 |
West , et al. |
April 22, 2014 |
Predicting human developmental toxicity of pharmaceuticals using
human stem-like cells and metabolomics
Abstract
The invention provides biomarker profiles of metabolites and
methods for screening chemical compounds including pharmaceutical
agents, lead and candidate drug compounds and other chemicals using
human stem-like cells (hSLCs) or lineage-specific cells produced
therefrom. The inventive methods are useful for testing toxicity,
particularly developmental toxicity and detecting teratogenic
effects of such chemical compounds. Specifically, a more predictive
developmental toxicity model, based on an in vitro method that
utilizes both hSLCs and metabolomics to discover biomarkers of
developmental toxicity is disclosed.
Inventors: |
West; Paul R. (Arena, WI),
Weir-Hauptman; April M. (Madison, WI), Smith; Alan M.
(Madison, WI), Donley; Elizabeth L. R. (Madison, WI),
Cezar; Gabriela G. (Madison, WI) |
Applicant: |
Name |
City |
State |
Country |
Type |
West; Paul R.
Weir-Hauptman; April M.
Smith; Alan M.
Donley; Elizabeth L. R.
Cezar; Gabriela G. |
Arena
Madison
Madison
Madison
Madison |
WI
WI
WI
WI
WI |
US
US
US
US
US |
|
|
Assignee: |
Stemina Biomarker Discovery,
Inc. (Madison, WI)
|
Family
ID: |
44673582 |
Appl.
No.: |
13/069,326 |
Filed: |
March 22, 2011 |
Prior Publication Data
|
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|
|
Document
Identifier |
Publication Date |
|
US 20110312019 A1 |
Dec 22, 2011 |
|
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
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61316165 |
Mar 22, 2010 |
|
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61394426 |
Oct 19, 2010 |
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Current U.S.
Class: |
435/6.13;
435/325 |
Current CPC
Class: |
G01N
33/6848 (20130101); G01N 33/5073 (20130101); G01N
33/5014 (20130101) |
Current International
Class: |
C12Q
1/68 (20060101); C12N 5/00 (20060101); C12N
5/02 (20060101) |
Field of
Search: |
;435/6.13,325 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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WO 02/066635 |
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Aug 2002 |
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WO 03/005628 |
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WO 03/018760 |
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Mar 2003 |
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WO |
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WO 2004/059293 |
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WO |
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WO 2004/065616 |
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Aug 2004 |
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WO |
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WO 2005/005621 |
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WO |
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WO 2005/005662 |
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WO |
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WO 2005/080551 |
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Sep 2005 |
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WO |
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WO 2006/090185 |
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WO |
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WO 2006/112841 |
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WO |
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WO 2006/121952 |
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WO |
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WO 2007/120699 |
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WO |
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WO 2008/021515 |
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WO |
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WO 2008/025016 |
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Feb 2008 |
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WO |
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WO 2008/140463 |
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Nov 2008 |
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WO |
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WO 2009/151967 |
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Dec 2009 |
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WO |
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|
Primary Examiner: Ton; Thaian N
Attorney, Agent or Firm: Mueting, Raasch & Gebhardt,
P.A.
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of priority under 35 U.S.C.
.sctn.119(e) of U.S. Ser. No. 61/316,165, filed Mar. 22, 2010, and
U.S. Ser. No. 61/394,426, filed Oct. 19, 2010, the entire contents
of which are incorporated herein by reference.
Claims
What is claimed is:
1. A method of screening the teratogenicity of a test compound, the
method comprising the steps of: (a) culturing human stem cell-like
cells (hSLCs): (i) in the presence of the test compound; and (ii)
in the absence of the test compound; (b) determining the fold
change in arginine associated with hSLCs cultured in the presence
of the test compound in comparison with hSLCs cultured in the
absence of the test compound; (c) determining the fold change in
asymmetric dimethyl arginine (ADMA) associated with hSLCs cultured
in the presence of the test compound in comparison with hSLCs
cultured in the absence of the test compound; (d) determining the
ratio of the fold change in arginine to the fold change in ADMA,
wherein: (i) a ratio of less than 0.9 or greater than 1.1 is
indicative of the teratogenicity of the test compound; and (ii) a
ratio of greater than 0.9 and less than 1.1 is indicative of the
non-teratogenicity of the test compound.
2. The method of claim 1, wherein the hSLCs are further cultured
during step a) in the presence of a known non-teratogenic
compound.
3. The method of claim 1, wherein the hSLCs comprise human
embryonic stem cells (hESCs), human induced pluripotent (iPS)
cells, or human embryoid bodies.
4. The method of claim 1, wherein metabolites are identified using
a physical separation method.
5. The method of claim 4, wherein the physical separation method is
mass spectrometry.
6. The method according to claim 5, wherein the mass spectrometry
is liquid chromatography/electro spray ionization mass
spectrometry.
7. The method of claim 1, further comprising determining a fold
change in one or more of succinic acid, gamma-aminobutyric acid
(GABA), isoleucine, aspartic acid, malic acid, glutamic acid and
histidine.
8. The method of claim 1, further comprising determining a fold
change in five or more of the small molecules listed in Table
8.
9. The method of claim 1, further comprising determining a fold
change in ten or more of the small molecules listed in Table 8.
10. The method of claim 1, further comprising determining a fold
change in one or more of the small molecules listed in Table
10.
11. The method according to claim 1, wherein the hSLCs comprise
human embryonic stem cells (hESCs), human induced pluripotent (iPS)
cells, or human embryoid bodies.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention provides methods for toxicological screening of
pharmaceuticals and other chemical compounds. The invention
specifically provides assays that involve multipotent human
stem-like cells (hSLCs), as well as methods for using these cells
to detect developmental toxicity or teratogenic effects of
pharmaceutical compounds and other chemicals. More particularly,
the invention provides an in vitro means for analyzing toxicity of
compounds predictive of their toxicity during human development.
Candidate predictive biomarkers for toxic or teratogenic effects
are also identified and provided herein.
2. Background Art
Birth defects are a leading cause of infant morbidity and pediatric
disorders in the United States, affecting 1 in every 33 infants
born (Brent & Beckman, 1990, Bull NY Acad Med 66: 123-63;
Rosano et al., 2000, J. Epidemiology Community Health 54:660-66),
or approximately 125,000 newborns per year. It is understood that
developmental toxicity can cause birth defects, and can generate
embryonic lethality, intrauterine growth restriction (IUGR),
dysmorphogenesis (such as skeletal malformations), and functional
toxicity, which can lead to cognitive disorders such as autism.
There is an increasing concern about the role that chemical
exposure can play in the onset of these disorders. Indeed, it is
estimated that 5% to 10% of all birth defects are caused by in
utero exposure to known teratogenic agents which induce
developmental abnormalities in the fetus (Beckman & Brent,
1984, Annu Rev Pharmacol 24: 483-500).
Concern exists that chemical exposure may be playing a significant
and preventable role in producing birth defects (Claudio et al.,
2001, Environm Health Perspect 109: A254-A261). This concern has
been difficult to evaluate, however, since the art has lacked a
robust and efficient model for testing developmental toxicity for
the more than 80,000 chemicals in the market, plus the new 2,000
compounds introduced annually (General Accounting Office (GAO),
1994, Toxic Substances Control Act: Preliminary Observations on
Legislative Changes to Make TSCA More Effective, Testimony, Jul.
13, 1994, GAO/T-RCED-94-263). Fewer than 5% of these compounds have
been tested for reproductive outcomes and even fewer for
developmental toxicity (Environmental Protective Agency (EPA),
1998, Chemical Hazard Data Availability Study, Office of Pollution
Prevention and Toxins). Although some attempts have been made to
use animal model systems to assess toxicity (Piersma, 2004,
Toxicology Letters 149:147-53), inherent differences in the
sensitivity of humans in utero have limited the predictive
usefulness of such models. Development of a human-based cell model
system would have an enormous impact in drug development and risk
assessment of chemicals.
Toxicity, particularly developmental toxicity, is also a major
obstacle in the progression of compounds through the drug
development process. Currently, toxicity testing is conducted on
animal models as a means to predict adverse effects of compound
exposure, particularly on development and organogenesis in human
embryos and fetuses. The most prevalent models that contribute to
FDA approval of investigational new drugs are whole animal studies
in rabbits and rats (Piersma, 2004, Toxicology Letters 149:
147-53). In vivo studies rely on administration of compounds to
pregnant animals at different stages of pregnancy and
embryonic/fetal development (first week of gestation, organogenesis
stage and full gestation length). However, these in vivo animal
models are limited by a lack of biological correlation between
animal and human responses to chemical compounds during development
due to differences in biochemical pathways. Species differences are
often manifested in trends such as dose sensitivity and
pharmacokinetic processing of compounds. According to the reported
literature, animal models are approximately 60% efficient in
predicting human developmental response to compounds (Greaves et
al., 2004, Nat Rev Drug Discov 3:226-36). Thus, human-directed
predictive in vitro models present an opportunity to reduce the
costs of new drug development and enable safer drugs.
In vitro models have been employed in the drug industry for over 20
years (Huuskonen, 2005, Toxicology & Applied Pharm
207:S495-S500). Many of the current in vitro assays involve
differentiation models using primary cell cultures or immortalized
cells lines (Huuskonen, 2005, Toxicology & Applied Pharm
207:S495-S500). Unfortunately, these models differ significantly
from their in vivo counterparts in their ability to accurately
assess development toxicity. In particular, the ECVAM initiative
(European Center for Validation of Alternative Methods) has used
mouse embryonic stem cells as a screening system for predictive
developmental toxicology. The embryonic stem cell test (EST) has
been able to predict the teratogenicity of 78% of the drugs tested,
and the test was reported to be able to differentiate strong
teratogens from moderate/weak or non-embryotoxic compounds
(Spielmann et al., 1997, In vitro Toxicology 10:119-27). This model
is limited in part because toxicological endpoints are defined only
for compounds that impair cardiac differentiation. This model also
fails to account for interspecies developmental differences between
mice and humans, and so does not fully address the need in the art
for human-specific model systems.
Thus there remains a need in the art for a human cell derived in
vitro method for reliably determining developmental toxicity in
pharmaceutical agents and other chemical compounds. There also is a
need in the art to better understand human development and its
perturbation by toxins and other developmental disrupting agents,
to assist clinical management of acquired congenital disorders and
the many diseases that share these biochemical pathways, such as
cancer. Human derived cell based systems increase the probability
of identifying biomarkers of toxicity that may both predict
toxicity as well as identify toxicity caused by other diseases.
The association of metabolomics and human embryonic stem cells
(hESCs) has led to a more effective in vitro human model to predict
developmental toxicity. hESCs were first derived from the inner
cell mass of blastocysts (Thomson et al. 1998). Given the human
embryonic origin of these cells, an in vitro teratogenicity test
using hESCs is likely to produce more accurate human endpoints,
while at the same time reducing cost and time and increasing
predictability over animal studies. Metabolomics assesses
functional changes in biochemical pathways by detecting changes to
the dynamic set of small molecules that comprise the metabolome.
The feasibility of metabolomics in biomarker discovery has been
demonstrated by multiple studies (Cezar et al. 2007, Tan et al.
1998, Sabatine et al. 2005, Barr et al. 2003, Qu et al. 2000).
However, there is an unmet need to develop more accurate methods
for human developmental toxicity screening and the establishment of
a highly predictive in vitro system for predicting chemical
toxicity during early human development.
The present study discloses the establishment of such a system. The
present invention further provides for the assessment of a
plurality of small molecules, preferably secreted or excreted from
human stem-like cells (hSLCs), and is determined and correlated
with health and disease or insult state.
The present invention provides a high-throughput developmental
toxicity screen that is more predictive than currently available
assays and which offers quantitative human endpoints.
BRIEF SUMMARY OF THE INVENTION
The present invention provides reagents and methods for more
reliable in vitro screening of toxicity and teratogenicity of
pharmaceutical and non-pharmaceutical chemicals on hSLCs.
The invention provides human-specific in vitro methods for reliably
determining toxicity, particularly developmental toxicity and
teratogenicity of pharmaceuticals and other chemical compounds
using hSLCs. As provided herein, hSLCs are useful for assessing
toxic effects of chemical compounds, particularly said toxic and
teratogenic effects on human development, thus overcoming the
limitations associated with interspecies animal models.
In particular, the invention demonstrates that metabolite profiles
of hSLCs are altered in response to known disruptors of human
development. The invention further shows that the hSLC metabolome
is a source of human biomarkers for disease and toxic response.
Thus, the hSLC and metabolomics based model of the present
invention offers a significant advantage over other studies that
use mouse or zebra fish-based models to determine toxicity and
teratogenicity of chemical compounds in that the present invention
utilizes an all human system and human biomarkers to understand the
mechanisms of human developmental toxicity.
In one embodiment, the invention discloses a method of predicting
teratogenicity of a test compound, comprising the steps of:
a) culturing hSLCs: i) in the presence of a first known teratogenic
compound; and ii) in the absence of the first known teratogenic
compound;
b) detecting a plurality of metabolites having a molecular weight
of less than about 3000 Daltons associated with hSLCs exposed to
the first known teratogenic compound in comparison with hSLCs not
exposed to the first known teratogenic compound in order to
identify a difference in metabolic response of hSLCs exposed to the
first known teratogenic compound in comparison with hSLCs not
exposed to the first known teratogenic compound;
c) analyzing the difference in metabolic response in order to
generate a set of mass features associated with exposure of hSLCs
to the first teratogenic compound;
d) repeating steps a)-c) multiple times, each time with a different
known teratogenic compound;
e) grouping mass features generated from each exposure to a
teratogenic compound to obtain a first reference profile of mass
features;
f) comparing a profile of mass features generated upon exposure of
hSLCs to a test compound with the first reference profile to
predict the teratogenicity of the test compound;
g) if the test compound is predicted to be a teratogen, adding the
profile of mass features to the first reference profile to obtain a
second reference profile, wherein the predictive accuracy of the
second reference profile is greater than the predictive accuracy of
the first reference profile; and
h) repeating steps f) and g) multiple times, each time with a
different test compound to obtain a final reference profile.
In another embodiment, the invention discloses a method for
classifying a test compound as a teratogen, the method comprising
the steps of:
a) culturing hSLCs: i) in the presence of the test compound; and
ii) in the absence of the test compound;
b) identifying a difference in metabolic response of hSLCs in the
presence of the test compound in comparison with hSLCs cultured in
the absence of the test compound by measuring a plurality of
metabolites having a molecular weight of less than about 3000
Daltons associated with hSLCs, wherein a difference in the
plurality of metabolites associated with hSLCs cultured in the
presence of the test compound versus hSLCs cultured in the absence
of the test compound indicates a difference in metabolic response;
and
c) determining the metabolic response of hSLCs involving a first
metabolite to the metabolic response of hSLCs involving a second
metabolite, wherein i) the first metabolite is a precursor of the
second metabolite; or ii) the first metabolite is an amino acid and
the second metabolite is an inhibitor of the metabolism of the
amino acid,
and wherein a difference in the metabolic response of hSLCs
involving the first metabolite to the metabolic response of hSLCs
involving the second metabolite is indicative of the test compound
being a teratogen.
In yet another embodiment, the invention discloses a method of
classifying a test compound as a teratogen or a non-teratogen,
comprising the steps of:
a) culturing hSLCs: i) in the presence of the test compound; and
ii) in the absence of the test compound;
b) determining the fold change in arginine associated with hSLCs
cultured in the presence of the test compound in comparison with
hSLCs cultured in the absence of the test compound;
c) determining the fold change in asymmetric dimethyl arginine
(ADMA) associated with hSLCs cultured in the presence of the test
compound in comparison with hSLCs cultured in the absence of the
test compound;
d) determining the ratio of the fold change in arginine to the fold
change in ADMA, wherein: i) a ratio of less than at least about 0.9
or greater than at least about 1.1 is indicative of the
teratogenicity of the test compound; and ii) a ratio of greater
than at least about 0.9 and less than at least about 1.1 is
indicative of the non-teratogenicity of the test compound.
In a further embodiment, the invention discloses a method for
validating a test compound as a teratogen, comprising:
a) providing, in solid form, a set of metabolites having a
molecular weight of less than about 3000 Daltons, wherein the
metabolites are differentially metabolized by hSLCs cultured in the
presence of one or more known teratogenic compounds in comparison
with hSLCs cultured in the absence of a teratogenic compound;
b) resuspending the set of metabolites in a predetermined volume of
a physiologically suitable buffer, wherein the final concentration
of each metabolite in the buffer is identical to the concentration
of that metabolite associated with hSLCs cultured in the presence
of one or more known teratogenic compounds;
c) generating a reference profile of the metabolites; and
d) comparing a profile of mass features generated upon exposure of
hSLCs to the test compound with the reference profile of
metabolites in order to validate the teratogenicity of the test
compound.
In yet another embodiment, the invention discloses a method of
identifying a metabolic effect of a teratogenic compound,
comprising:
a) culturing hSLCs: i) in the presence of the teratogenic compound;
and ii) in the absence of the teratogenic compound;
b) detecting a plurality of metabolites having a molecular weight
of less than about 3000 Daltons associated with hSLCs exposed to
the teratogenic compound in comparison with hSLCs not exposed to
the teratogenic compound in order to identify a difference in
metabolic response of hSLCs exposed to the teratogenic compound in
comparison with hSLCs not exposed to the teratogenic compound;
c) mapping the plurality of metabolites to one or more metabolic
networks; and
d) identifying a metabolic effect of the teratogenic compound when
the plurality of metabolites are identical to metabolites affected
by a known disruption of the one or more metabolic networks.
Specific preferred embodiments of the present invention will become
evident from the following more detailed description of certain
preferred embodiments and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES
FIG. 1 illustrates the experimental design used in the present
study. Three plate replicates with three well replicates were used
for controls (cells with undosed media) and experimental cells
(dosed cells). Three well replicates were used for media control
(no cells, undosed media) and dosed media controls (no cells, dosed
media).
FIG. 2 illustrates cell viability data that has been normalized to
control, undosed cells.
FIG. 3 shows multidimensional scaling plot of the of Random Forest
model (similarity metric) showing a clear separation of drugs based
on teratogenicity. The circled drug treatments mark rifampicin and
accutane that were misclassified as non-teratogens by the random
forest model. Gray=Teratogen, Black=Non-Teratogen, point=first
letter of drug.
FIG. 4 illustrates a receiver operating characteristic (ROC) curve
based on the 18-feature refined random forest model.
FIG. 5 depicts a specific step of the urea cycle involving
metabolism of L-arginine to L-citrulline. NO is released when the
enzyme nitric oxide synthase (NOS) oxidizes L-arginine to
L-citrulline. Dimethylarginine inhibits nitric oxide synthase.
Nitric oxide has been shown to induce Neural Tube Defects (NTD) in
rat embryos.
FIG. 6 illustrates the metabolic network relationships between the
metabolites found in this study.
FIG. 7 illustrates the experimental design in 96-well plates for
dosing experiments used in the present study.
FIG. 8 depicts data preprocessing flow diagram outlier and overview
of the filters applied during data processing.
FIG. 9 depicts an overview of the statistical analysis process.
FIG. 10 depicts a viability assay. Cytotoxicity ratios normalized
to the untreated cells (controls) present for each 96-well plate.
Bars marked with an asterisk indicate a statistically significant
decrease (p value <0.05) in viability:cytotoxicity ratios by a
Welch T-test. Chemical compound treatments ST003G-74-A, ST003G-80G,
and STO003G-81H exhibit unexpected viability results where low dose
appears more toxic than 10.times.. Drug treatments ST003G-84K, and
ST003G-85L do not exhibit a decrease in viability associated with
an increase in dosage.
FIG. 11 depicts the nicotinate and nicotinamide metabolic network.
In this figure and FIGS. 12-28 that follow, all of the features
across all 12 treatment compounds that were putatively annotated
with KEGG ID's and identified as significant in the networks
enrichment analysis were reviewed for fold changes and marked with
black circles in the network diagrams. Isobaric enzymes are marked
with grey circles. Enzymes are identified with EC codes and
identified human enzyme activity is highlighted in grey.
FIG. 12 depicts the pantothenate and coenzyme A biosynthesis
network, wherein the respective networks are modified as disclosed
herein.
FIG. 13 depicts the glutathione metabolic network, wherein the
network is modified according to the present disclosure.
FIG. 14 depicts the arginine and proline metabolic network, wherein
the network is modified according to the present disclosure.
FIG. 15 depicts the cysteine and methionine metabolic network,
wherein the network is modified according to the present
disclosure.
FIG. 16 depicts the pentose phosphate network, wherein the network
is modified according to the present disclosure.
FIG. 17 depicts the pentose and glucoronate interconversions
network, wherein the network is modified according to the present
disclosure.
FIG. 18 depicts the galactose metabolic network, wherein the
network is modified according to the present disclosure.
FIG. 19 depicts the ascorbate and aldarate metabolic network,
wherein the network is modified according to the present
disclosure.
FIG. 20 depicts the purine and pyrimidine metabolic networks,
wherein the network is modified according to the present
disclosure.
FIG. 21 depicts the valine, leucine, and isoleucine degradation
network, wherein the network is modified according to the present
disclosure.
FIG. 22 depicts the lysine biosynthesis and lysine degradation
networks, wherein the network is modified according to the present
disclosure.
FIG. 23 depicts the amino sugar and nucleotide sugar metabolic
network, wherein the network is modified according to the present
disclosure.
FIG. 24 depicts the pyruvate metabolic network, wherein the network
is modified according to the present disclosure.
FIG. 25 depicts the propanoate metabolism and thiamine metabolic
networks, wherein the respective networks are modified as disclosed
herein.
FIG. 26 depicts the vitamin B6 metabolic network, wherein the
network is modified according to the present disclosure.
FIG. 27 depicts the nicotinate and nicotinamide metabolic networks,
wherein the respective networks are modified as disclosed
herein.
FIG. 28 depicts the folate biosynthesis network, wherein the
network is modified according to the present disclosure.
FIG. 29 illustrates cell viability data following doxylamine dosing
of hES cells.
The present invention will now be described with reference to the
accompanying drawings. It is understood that the drawings of the
present application are not necessarily drawn to scale and that
these figures and illustrations merely illustrate, but do not
limit, the present invention.
DETAILED DESCRIPTION OF THE INVENTION
The invention provides reagents that are hSLCs, or hESC-derived
lineage-specific cells, such as neural stem cells, neural precursor
cells and neural cells produced therefrom, for assessing
developmental toxicity using the human embryonic stem cell
metabolome. hESCs are pluripotent, self-renewing cells isolated
directly from preimplantation human embryos that recapitulate
organogenesis in vitro. Lineage-specific precursor cells are
derived from hESCs and have entered a specific cellular lineage,
but yet remain multipotent with regard to cell type within that
specific lineage. For example, neural precursors have committed to
neural differentiation but yet remain unrestricted as to its neural
cell type. Biochemical networks of human development and disease
are active in hSLCs, because they recapitulate differentiation into
functional somatic cells. Disruption of these networks during
development contributes to disorders such as neural tube defects
(NTDs) and cognitive impairment. Environmental agents, namely
chemicals or drugs, participate in the ontogenesis of certain
acquired congenital disorders.
This specification discloses one or more embodiments that
incorporate the features of this invention. The disclosed
embodiment(s) merely exemplify the invention. The scope of the
invention is not limited to the disclosed embodiment(s). The
invention is defined by the claims appended hereto.
In the following description, for purposes of explanation, specific
numbers, parameters and reagents are set forth in order to provide
a thorough understanding of the invention. It is understood,
however, that the invention can be practiced without these specific
details. In some instances, well-known features can be omitted or
simplified so as not to obscure the present invention.
The embodiment(s) described, and references in the specification to
"one embodiment", "an embodiment of the invention", "an
embodiment", "an example embodiment", etc., indicate that the
embodiment(s) described may include a particular feature,
structure, or characteristic, but every embodiment may not
necessarily include the particular feature, structure, or
characteristic. Moreover, such phrases are not necessarily
referring to the same embodiment. Further, when a particular
feature, structure, or characteristic is described in connection
with an embodiment, it is understood that it is within the
knowledge of one skilled in the art to effect such feature,
structure, or characteristic in connection with other embodiments
whether or not explicitly described.
The description of "a" or "an" item herein may refer to a single
item or multiple items. For example, the description of a feature,
a protein, a biological fluid, or a classifier may refer to a
single feature, a protein, a biological fluid, or a classifier.
Alternatively, the description of a feature, a protein, a
biological fluid, or a classifier may refer to multiple features,
proteins, biological fluids, or classifiers. Thus, as used herein,
"a" or "an" may be singular or plural. Similarly, references to and
descriptions of plural items may refer to single items.
It is understood that wherever embodiments are described herein
with the language "comprising," otherwise analogous embodiments
described in terms of "consisting of" and/or "consisting
essentially of" are also provided.
The specification describes methods and kits for predicting and
assaying teratogenicity of test compounds as well as methods for
assaying test compounds for neural development disruption by
detecting a specific set of purified cellular metabolites having a
molecular weight of less than about 3000 Daltons that are
differentially hSLCs cultured in the presence of known teratogenic
compounds in comparison with hSLCs cultured in the absence the
known teratogenic compounds. In certain embodiments, the
metabolites have a molecular weight from about 50 to about 3000
Daltons. Specific exemplary embodiments for detecting marker
proteins in the serum are provided herein. However, based on the
teaching and guidance presented herein, it is understood that it is
within the knowledge of one skilled in the art to readily adapt the
methods described herein to.
Definitions
The metabolome, defined as the total dynamic set of cellular
metabolites created through cellular metabolism, is a product of
health or disease/insult states. Metabolites include but are not
limited to sugars, organic acids, amino acids and fatty acids,
particularly those species secreted, excreted, consumed, or
identified by the cells, or those metabolites that are fluxed
through the cells, that participate in functional mechanisms of
cellular response to pathological or chemical insult. These
metabolites serve as biomarkers of disease or toxic response and
can be detected in biological fluids (Soga et al., 2006, J Biol
Chem 281:16768-78; Zhao et al., 2006, Birth Defects Res A Clin Mol
Teratol 76:230-6), including hSLC culture media. Importantly,
metabolomic profiling may confirm functional changes that are often
predicted by transcriptomics and proteomics.
Because it was known that hSLCs are highly sensitive to the culture
microenvironment (Levenstein et al., 2005, Stem Cells 24: 568-574;
Li et al., 2005, Biotechnol Bioeng 91:688-698), their application
as a source of predictive biomarkers in response to chemical
compounds, including toxins, teratogens and particularly
pharmaceutical agents, drug lead compounds and candidate compounds
in drug development, and their usefulness in establishing in vitro
models of disease and development was uncertain, inter alia because
those of skill in the art could anticipate that exposure to an
exogenous chemicals could be highly detrimental to survival of
hSLCs and preclude obtaining useful information from them. This
concern has turned out not to be justified.
As used herein, the term "human stem-like cells (hSLCs)" is
intended to include pluripotent, undifferentiated hESCs, as well as
human induced pluripotent (iPS) cells, and human embryoid
bodies.
As used herein, the term "human embryonic stem cells (hESCs)" is
intended to include undifferentiated stem cells originally derived
from the inner cell mass of developing blastocysts, and
specifically pluripotent, undifferentiated human stem cells and
partially-differentiated cell types thereof (e.g., downstream
progenitors of differentiating hESC). As provided herein, in vitro
cultures of hESCs are pluripotent and not immortalized, and can be
induced to produce lineage-specific cells and differentiated cell
types using methods well-established in the art. In preferred
embodiments, hESCs useful in the practice of the methods of this
invention are derived from preimplantation blastocysts as described
by Thomson et al., in co-owned U.S. Pat. No. 6,200,806. Multiple
hESC lines are currently available in US and UK stem cell
banks.
As used herein, the term "human embryoid bodies" are aggregates of
cells derived from human embryonic stem cells. Cell aggregation is
imposed by hanging drop, plating upon non-tissue culture treated
plates or spinner flasks; either method prevents cells from
adhering to a surface to form the typical colony growth. Upon
aggregation, differentiation is initiated and the cells begin to a
limited extent to recapitulate embryonic development. Embryoid
bodies are composed of cells from all three germ layers: endoderm,
ectoderm and mesoderm.
As used herein, the term "human induced pluripotent stem cells",
commonly abbreviated as iPS cells are a type of pluripotent stem
cell artificially derived from a non-pluripotent cell, typically an
adult somatic cell, by inducing a forced expression of certain
genes. iPS cells are believed to be identical to natural
pluripotent stem cells, such as embryonic stem cells in many
respects, such as the expression of certain stem cell genes and
proteins, chromatin methylation patterns, doubling time, embryoid
body formation, teratoma formation, viable chimera formation, and
potency and differentiability.
In one embodiment, the cells of the present invention can also
include hSLC-derived lineage specific cells. The terms
"hSLC-derived lineage specific cells", "stem cell progenitor,"
"lineage-specific cell," "hSLC derived cell" and "differentiated
cell" as used herein are intended to encompass lineage-specific
cells that are differentiated from hSLCs such that the cells have
committed to a specific lineage of diminished pluripotency. For
example, hSLC-derived lineage specific cells are derived from hSLCs
and have entered a specific cellular lineage, but yet remain
multipotent with regard to cell type within that specific lineage.
The hSLC-derived lineage specific cells can include, for example,
neural stem cells, neural precursor cells, neural cells, cardiac
stem cells, cardiac precursor cells, cardiomyocytes, and the like.
In some embodiments, these hSLC-derived lineage-specific cells
remain undifferentiated with regard to final cell type. For
example, neuronal stem cells are derived from hSLCs and have
differentiated enough to commit to neuronal lineage. However, the
neuronal precursor retains "sternness" in that it retains the
potential to develop into any type of neuronal cell. Additional
cell types include terminally-differentiated cells derived from
hSLCs or lineage-specific precursor cells, for example neural
cells.
The term "cellular metabolite" and "metabolite" have been used
herein interchangeably. The terms "cellular metabolite" or
"metabolite" as used herein refer to any small molecule secreted,
excreted or identified by hSLCs or any small molecule that is
fluxed through hSLCs or lineage-specific precursor cells, for
example, neural cells. In preferred embodiments, cellular
metabolites or metabolites include but are not limited to sugars,
organic acids, amino acids, fatty acids, hormones, vitamins,
oligopeptides (less than about 100 amino acids in length), as well
as ionic fragments thereof. Cells can also be lysed in order to
measure cellular products present within the cell. In particular,
said metabolites are less than about 3000 Daltons in molecular
weight, and more particularly from about 50 to about 3000
Daltons.
The term "metabolic effect" of a teratogenic compound as used
herein refers to the difference in a plurality of metabolites of
one or more metabolic networks in hSLCs cultured in presence of the
teratogenic compound in comparison with hSLCs cultured in absence
of the teratogenic compound, or hSLCs cultured in presence of a
known non-teratogenic compound, wherein the plurality of
metabolites are identical to metabolites affected by a known
disruption of the one or more metabolic networks. In one
embodiment, the metabolites can be differentially expressed. In one
aspect, for example, the expression of the metabolites is increased
when exposed to a teratogenic compound and decreased when exposed
to a non-teratogenic compound. In another aspect, for example, the
metabolites are secreted when exposed to a teratogenic compound and
not secreted when exposed to a non-teratogenic compound.
The term "metabolic response" as used herein refers to a change
caused through alterations in enzyme activity (e.g. regulation by
allosteric, covalent modification, or protein processing), enzyme
abundance, non-enzymatic chemical reactions, cellular transporters,
and action of enzymes in the extracellular space leading to changes
in abundance of one or more metabolites or flux of media components
in response to an experimental treatment. The response can be
measured both by changes in abundance of one or more metabolites in
the extracellular or intracellular environment.
In one embodiment, one or more of the measured metabolites is a
metabolite secreted from the hSLCs.
In one embodiment, one or more of the measured metabolites is a
metabolite excreted from the hSLCs.
In one embodiment, one or more of the measured metabolites is a
metabolite consumed by the hSLCs.
In one embodiment, one or more of the measured metabolites is a
metabolite identified by the hSLCs.
In one embodiment, the difference in metabolic response for the
secreted, excreted, consumed, or identified metabolite associated
with hSLCs cultured in the presence of a test compound or a known
teratogenic compound in comparison with hSLCs cultured in the
absence of a test compound or a known teratogenic compound is
determined by measuring the flux of the metabolite through the
hSLCs.
The term "flux" as used herein refers to the turnover of
metabolites by catabolism and/or anabolism through the metabolic
networks and networks of an organism. The metabolic footprint
observed by measuring the differential utilization of media
components following treatments of cultures is an example of
metabolic flux.
The term "identified" as used herein refers to cellular metabolites
that are secreted or consumed by hSLCs. The term also encompasses
cellular metabolites that are fluxed through hSLCs.
hSLCs are cultured according to the methods of the invention using
standard methods of cell culture well-known in the art, including,
for example those methods disclosed in Ludwig et al. (2006,
Feeder-independent culture of human embryonic stem cells, Nat
Methods 3: 637-46). In preferred embodiments, hSLCs are cultured in
the absence of a feeder cell layer during the practice of the
inventive methods; however, hSLCs can be cultured on feeder cell
layer prior to the practice of the methods of this invention.
The terms "administering" or "dosing" as used herein refer to
contacting in vitro cultures of hSLCs with a toxic, teratogenic, or
test chemical compound. In a preferred embodiment the dosage of the
compound is administered in an amount equivalent to levels achieved
or achievable in vivo, for example, in maternal circulation.
The phrases "identifying metabolites that are differentially
produced" or "detecting alterations in the cells or alternations in
metabolism" as used herein include but are not limited to
comparisons of treated hSLCs to untreated (control) cells (i.e.,
cells cultured in the presence (treated) or absence (untreated) of
a toxic, teratogenic, or test chemical compound. Detection or
measurement of variations in cellular metabolites, excreted or
secreted or metabolized in the medium therefrom, between treated
and untreated cells is included in this definition. In a preferred
embodiment, alterations in cells or cell activity are measured by
determining a profile of changes in cellular metabolites having a
molecular weight of less than 3000 Daltons, more particularly
between 50 and 3000 Daltons, in a treated versus untreated
cell.
The terms "metabolic pathway" or "metabolic network" or "metabolism
pathway" as used herein refers to a series of chemical reactions
occurring within a cell. In each pathway or network, a principal
compound is modified by one or more chemical or enzymatic
reactions. Moreover, a metabolic pathway or network can be composed
of a series of biochemical reactions connected by their
intermediates. The reactants (or substrates) of one reaction can be
the products of a previous reaction, and so on. Metabolic pathways
or networks are usually considered in one direction (although most
reactions are reversible, conditions in the cell are such that it
is thermodynamically more favorable for flux to be in one of the
directions). Enzymes catalyze the reactions of a metabolic pathway,
and often require dietary minerals, vitamins, and other cofactors
in order to function properly. Because of the many compounds that
may be involved, pathways can be quite elaborate. In addition, many
pathways can exist within a cell. This collection of pathways is
called the metabolic network. Metabolic pathways and networks are
important to the maintenance of homeostasis within an organism. In
one embodiment, a compound comprises one or more biological
molecules of a metabolic pathway or network that are modified by
one or more chemical or enzymatic reactions. In another embodiment
a compound comprises one or more products of a metabolic pathway or
network that are modified by one or more chemical or enzymatic
reactions. In another aspect a compound comprises one or more
intermediates of a metabolic pathway or network that are modified
by one or more chemical or enzymatic reactions. In yet another
embodiment a compound comprises one or more reactants of a
metabolic pathway or network that are modified by one or more
chemical or enzymatic reactions. Any person of skill in the art
would understand that a metabolic pathway or metabolic network, as
defined herein, includes one or more compounds associated with
anabolic and/or catabolic metabolism of a particular metabolite.
For example, glutathione pathway comprises products or reactants
associated with anabolic and/or catabolic metabolism of
glutathione.
The term "correlating" or "associating" or "pattern matching" as
used herein refers to the positive correlation, or association, or
matching of alterations of patterns in cellular metabolites
including but not limited to sugars, organic acids, amino acids,
fatty acids, and low molecular weight compounds excreted or
secreted from hSLCs, to an in vivo toxic response. The screened
cellular metabolites can be involved in a wide range of biochemical
networks in the cells and related to a variety of biological
activities including, but not limited to inflammation,
anti-inflammatory response, vasodilation, neuroprotection,
oxidative stress, antioxidant activity, DNA replication and cell
cycle control, methylation, and biosynthesis of, inter alia,
nucleotides, carbohydrates, amino acids and lipids, among others.
Alterations in specific subsets of cellular metabolites can
correspond to a particular metabolic or developmental network and
thus reveal effects of a test compound on in vivo development.
In one embodiment, cellular metabolites are identified using a
physical separation method.
The term "physical separation method" as used herein refers to any
method known to those with skill in the art sufficient to produce a
profile of changes and differences in small molecules produced in
hSLCs, contacted with a toxic, teratogenic or test chemical
compound according to the methods of this invention. In a preferred
embodiment, physical separation methods permit detection of
cellular metabolites including but not limited to sugars, organic
acids, amino acids, fatty acids, hormones, vitamins, and
oligopeptides, as well as ionic fragments thereof and low molecular
weight compounds (preferably with a molecular weight less than 3000
Daltons, and more particularly between 50 and 3000 Daltons). For
example, mass spectrometry can be used. In particular embodiments,
this analysis is performed by liquid chromatography/electrospray
ionization time of flight mass spectrometry (LC/ESI-TOF-MS),
however it will be understood that cellular metabolites as set
forth herein can be detected using alternative spectrometry methods
or other methods known in the art for analyzing these types of
cellular compounds in this size range.
The term "biomarker" as used herein refers to metabolites that
exhibit significant alterations between hSLCs cultured in the
presence of a test compound or a known teratogenic compound in
comparison with hSLCs cultured in the absence of the test compound
or the known teratogenic compound. In one embodiment, at least one
of the metabolites is secreted or excreted from the hSLCs or
consumed or identified by hSLCs in greater amounts in the presence
of the test compound or known teratogenic compound than in the
absence of the test compound or the known teratogenic compound. In
another embodiment, at least one of the cellular metabolites is
secreted or excreted from the hSLCs in lower amounts in the
presence of the test compound or known teratogenic compound than in
the absence of the test compound or the known teratogenic
compound.
In preferred embodiments, biomarkers are identified by methods
including LC/ESI-TOF-MS and QTOF-MS. Metabolomic biomarkers are
identified by their unique molecular mass and consistency with
which the marker is detected in response to a particular toxic,
teratogenic or test chemical compound; thus the actual identity of
the underlying compound that corresponds to the biomarker is not
required for the practice of this invention.
Alternatively, certain biomarkers can be identified by, for
example, gene expression analysis, including real-time PCR, RT-PCR,
Northern analysis, and in situ hybridization.
In addition, biomarkers can be identified using Mass Spectrometry
such as MALDI/TOF (time-of-flight), SELDI/TOF, liquid
chromatography-mass spectrometry (LC-MS), gas chromatography-mass
spectrometry (GC-MS), high performance liquid chromatography-mass
spectrometry (HPLC-MS), capillary electrophoresis-mass
spectrometry, nuclear magnetic resonance spectrometry, tandem mass
spectrometry (e.g., MS/MS, MS/MS/MS, ESI-MS/MS etc.), secondary ion
mass spectrometry (SIMS), or ion mobility spectrometry (e.g.
GC-IMS, IMS-MS, LC-IMS, LC-IMS-MS etc.).
Mass spectrometry methods are well known in the art and have been
used to quantify and/or identify biomolecules, such as proteins and
other cellular metabolites (see, e.g., Li et al., 2000; Rowley et
al., 2000; and Kuster and Mann, 1998).
In certain embodiments, a gas phase ion spectrophotometer is used.
In other embodiments, laser-desorption/ionization mass spectrometry
is used to identify biomarkers. Modern laser desorption/ionization
mass spectrometry ("LDI-MS") can be practiced in two main
variations: matrix assisted laser desorption/ionization ("MALDI")
mass spectrometry and surface-enhanced laser desorption/ionization
("SELDI").
In MALDI, the analyte (e.g. biomarkers) is mixed with a solution
containing a matrix, and a drop of the liquid is placed on the
surface of a substrate. The matrix solution then co-crystallizes
with the biomarkers. The substrate is inserted into the mass
spectrometer. Laser energy is directed to the substrate surface
where it desorbs and ionizes the proteins without significantly
fragmenting them. However, MALDI has limitations as an analytical
tool. It does not provide means for fractionating the biological
fluid, and the matrix material can interfere with detection,
especially for low molecular weight analytes.
In SELDI, the substrate surface is modified so that it is an active
participant in the desorption process. In one variant, the surface
is derivatized with adsorbent and/or capture reagents that
selectively bind the biomarker of interest. In another variant, the
surface is derivatized with energy absorbing molecules that are not
desorbed when struck with the laser. In another variant, the
surface is derivatized with molecules that bind the biomarker of
interest and that contain a photolytic bond that is broken upon
application of the laser. In each of these methods, the
derivatizing agent generally is localized to a specific location on
the substrate surface where the sample is applied. The two methods
can be combined by, for example, using a SELDI affinity surface to
capture an analyte (e.g. biomarker) and adding matrix-containing
liquid to the captured analyte to provide the energy absorbing
material.
For additional information regarding mass spectrometers, see, e.g.,
Principles of Instrumental Analysis, 3rd edition., Skoog, Saunders
College Publishing, Philadelphia, 1985; and Kirk-Othmer
Encyclopedia of Chemical Technology, 4.sup.th ed. Vol. 15 (John
Wiley & Sons, New York 1995), pp. 1071-1094.
In some embodiments, the data from mass spectrometry is represented
as a mass chromatogram. A "mass chromatogram" is a representation
of mass spectrometry data as a chromatogram, where the x-axis
represents time and the y-axis represents signal intensity. In one
aspect the mass chromatogram is a total ion current (TIC)
chromatogram. In another aspect, the mass chromatogram is a base
peak chromatogram. In other embodiments, the mass chromatogram is a
selected ion monitoring (SIM) chromatogram. In yet another
embodiment, the mass chromatogram is a selected reaction monitoring
(SRM) chromatogram. In a preferred embodiment, the mass
chromatogram is an extracted ion chromatogram (EIC).
In an EIC, a single feature is monitored throughout the entire run.
The total intensity or base peak intensity within a mass tolerance
window around a particular analyte's mass-to-charge ratio is
plotted at every point in the analysis. The size of the mass
tolerance window typically depends on the mass accuracy and mass
resolution of the instrument collecting the data. As used herein,
the term "feature" refers to a single small metabolite, or a
fragment of a metabolite. In some embodiments, the term feature may
also include noise upon further investigation.
Detection of the presence of a biomarker will typically involve
detection of signal intensity. This, in turn, can reflect the
quantity and character of a biomarker bound to the substrate. For
example, in certain embodiments, the signal strength of peak values
from spectra of a first sample and a second sample can be compared
(e.g., visually, by computer analysis etc.) to determine the
relative amounts of particular biomarkers. Software programs such
as the Biomarker Wizard program (Ciphergen Biosystems, Inc.,
Fremont, Calif.) can be used to aid in analyzing mass spectra. The
mass spectrometers and their techniques are well known.
A person skilled in the art understands that any of the components
of a mass spectrometer, e.g., desorption source, mass analyzer,
detect, etc., and varied sample preparations can be combined with
other suitable components or preparations described herein, or to
those known in the art. For example, in some embodiments a control
sample may contain heavy atoms, e.g. .sup.13C, thereby permitting
the test sample to be mixed with the known control sample in the
same mass spectrometry nm. Good stable isotopic labeling is
included.
In one embodiment, a laser desorption time-of-flight (TOF) mass
spectrometer is used. In laser desorption mass spectrometry, a
substrate with a bound marker is introduced into an inlet system.
The marker is desorbed and ionized into the gas phase by laser from
the ionization source. The ions generated are collected by an ion
optic assembly, and then in a time-of-flight mass analyzer, ions
are accelerated through a short high voltage field and let drift
into a high vacuum chamber. At the far end of the high vacuum
chamber, the accelerated ions strike a sensitive detector surface
at a different time. Since the time-of-flight is a function of the
mass of the ions, the elapsed time between ion formation and ion
detector impact can be used to identify the presence or absence of
molecules of specific mass to charge ratio.
In one embodiment of the invention, levels of biomarkers are
detected by MALDI-TOF mass spectrometry.
Methods of detecting biomarkers also include the use of surface
plasmon resonance (SPR). The SPR biosensing technology has been
combined with MALDI-TOF mass spectrometry for the desorption and
identification of biomarkers.
Data for statistical analysis can be extracted from chromatograms
(spectra of mass signals) using softwares for statistical methods
known in the art. "Statistics" is the science of making effective
use of numerical data relating to groups of individuals or
experiments. Methods for statistical analysis are well-known in the
art.
In one embodiment a computer is used for statistical analysis.
In one embodiment, the Agilent MassProfiler or
MassProfilerProfessional software is used for statistical analysis.
In another embodiment, the Agilent MassHunter software Qual
software is used for statistical analysis. In other embodiments,
alternative statistical analysis methods can be used. Such other
statistical methods include the Analysis of Variance (ANOVA) test,
Chi-square test, Correlation test, Factor analysis test,
Mann-Whitney U test, Mean square weighted derivation (MSWD),
Pearson product-moment correlation coefficient, Regression
analysis, Spearman's rank correlation coefficient, Student's T
test, Welch's T-test, Tukey's test, and Time series analysis.
In different embodiments signals from mass spectrometry can be
transformed in different ways to improve the performance of the
method. Either individual signals or summaries of the distributions
of signals (such as mean, median or variance) can be so
transformed. Possible transformations include taking the logarithm,
taking some positive or negative power, for example the square root
or inverse, or taking the arcsin (Myers, Classical and Modem
Regression with Applications, 2.sup.nd edition, Duxbury Press,
1990).
In different embodiments, statistical classification algorithms are
used to create a classification model in order to predict
teratogenicity and non-teratogenicity of test compounds. Machine
learning-based classifiers have been applied in various fields such
as machine perception, medical diagnosis, bioinformatics,
brain-machine interfaces, classifying DNA sequences, and object
recognition in computer vision. Learning-based classifiers have
proven to be highly efficient in solving some biological
problems.
As used herein, "classification" is the process of learning to
separate data points into different classes by finding common
features between collected data points which are within known
classes. In statistics, classification is the problem of
identifying the sub-population to which new observations belong,
where the identify of the sub-population is unknown, on the basis
of a training set of data containing observations whose
sub-population is known. Thus the requirement is that new
individual items are placed into groups based on quantitative
information on one or more measurements, traits or characteristics,
etc) and based on the training set in which previously decided
groupings are already established. Classification problem has many
applications. In some cases, it is employed as a data mining
procedure, while in others more detailed statistical modeling is
undertaken.
As used herein, a "classifier" is a method, algorithm, computer
program, or system for performing data classification. Examples of
widely used classifiers include, but are not limited to, the Neural
network (multi-layer perceptron), Support vector machines,
k-nearest neighbors, Gaussian mixture model, Gaussian, naive Bayes,
Decision tree, and RBF classifiers.
In some embodiments, classification models to predict
teratogenicity and non-teratogenicity of test compounds are created
using either Linear classifiers (for e.g., partial least squares
determinant analysis (PLS-DA), Fisher's linear discriminant,
Logistic regression, Naive Bayes classifier, Perceptron), Support
vector machines (for e.g., least squares support vector machines),
quadratic classifiers, Kernel estimation (for e.g., k-nearest
neighbor), Boosting, Decision trees (for e.g., Random forests),
Neural networks, Bayesian networks, Hidden Markov models, or
Learning vector quantization.
In a preferred embodiment, the Random forest model is used to
create a classification model in order to predict teratogenicity
and non-teratogenicity of test compounds. Random forest (or random
forests) is an ensemble classifier that consists of many decision
trees and outputs the class that is the mode of the class's output
by individual trees. A "decision tree" is a decision support tool
that uses a tree-like graph or model of decisions and their
possible consequences, including chance event outcomes, resource
costs, and utility. It is one way to display an algorithm. Decision
trees are commonly used in operations research, specifically in
decision analysis, to help identify a strategy most likely to reach
a goal. Another use of decision trees is as a descriptive means for
calculating conditional probabilities. Decision tree learning, used
in statistics, data mining and machine learning, uses a decision
tree as a predictive model which maps observations about an item to
conclusions about the item's target value. More descriptive names
for such tree models are classification trees or regression trees.
In these tree structures, leaves represent classifications and
branches represent conjunctions of features that lead to those
classifications.
As used herein, a "training set" is a set of data used in various
areas of information science to discover potentially predictive
relationships. Training sets are used in artificial intelligence,
machine learning, genetic programming, intelligent systems, and
statistics. In all these fields, a training set has much the same
role and is often used in conjunction with a test set.
As used herein, a "test set" is a set of data used in various areas
of information science to assess the strength and utility of a
predictive relationship. Test sets are used in artificial
intelligence, machine learning, genetic programming, intelligent
systems, and statistics. In all these fields, a test set has much
the same role.
As used herein, "regression analysis" includes any techniques for
modelling and analyzing several variables, when the focus is on the
relationship between a dependent variable and one or more
independent variables. More specifically, regression analysis helps
understand how the typical value of the dependent variable changes
when any one of the independent variables is varied, while the
other independent variables are held fixed. Most commonly,
regression analysis estimates the conditional expectation of the
dependent variable given the independent variables--that is, the
average value of the dependent variable when the independent
variables are held fixed. Less commonly, the focus is on a
quantile, or other location parameter of the conditional
distribution of the dependent variable given the independent
variables. In all cases, the estimation target is a function of the
independent variables called the regression function. In regression
analysis, it is also of interest to characterize the variation of
the dependent variable around the regression function, which can be
described by a probability distribution. Regression analysis is
widely used for prediction and forecasting, where its use has
substantial overlap with the field of machine learning. Regression
analysis is also used to understand which among the independent
variables are related to the dependent variable, and to explore the
forms of these relationships. In restricted circumstances,
regression analysis can be used to infer causal relationships
between the independent and dependent variables. A large body of
techniques for carrying out regression analysis has been developed.
Familiar methods such as linear regression and ordinary least
squares regression are parametric, in that the regression function
is defined in terms of a finite number of unknown parameters that
are estimated from the data. Nonparametric regression refers to
techniques that allow the regression function to lie in a specified
set of functions, which may be infinite-dimensional.
"Sensitivity" and "specificity" are statistical measures of the
performance of a binary classification test. Sensitivity (also
called recall rate in some fields) measures the proportion of
actual positives which are correctly identified as such (e.g. the
percentage of sick people who are correctly identified as having
the condition). Specificity measures the proportion of negatives
which are correctly identified (e.g. the percentage of healthy
people who are correctly identified as not having the condition).
These two measures are closely related to the concepts of type I
and type II errors. A theoretical, optimal prediction can achieve
100% sensitivity (i.e. predict all people from the sick group as
sick) and 100% specificity (i.e. not predict anyone from the
healthy group as sick). A specificity of 100% means that the test
recognizes all actual negatives--for example, in a test for a
certain disease, all disease free people will be recognized as
disease free. A sensitivity of 100% means that the test recognizes
all actual positives--for example, all sick people are recognized
as being ill. Thus, in contrast to a high specificity test,
negative results in a high sensitivity test are used to rule out
the disease. A positive result in a high specificity test can
confirm the presence of disease. However, from a theoretical point
of view, a 100%-specific test standard can also be ascribed to a
`bogus` test kit whereby the test simply always indicates negative.
Therefore the specificity alone does not tell us how well the test
recognizes positive cases. A knowledge of sensitivity is also
required. For any test, there is usually a trade-off between the
measures. For example, in a diagnostic assay in which one is
testing for people who have a certain condition, the assay may be
set to overlook a certain percentage of sick people who are
correctly identified as having the condition (low specificity), in
order to reduce the risk of missing the percentage of healthy
people who are correctly identified as not having the condition
(high sensitivity). This trade-off can be represented graphically
using a receiver operating characteristic (ROC) curve.
The "accuracy" of a measurement system is the degree of closeness
of measurements of a quantity to its actual (true) value. The
"precision" of a measurement system, also called reproducibility or
repeatability, is the degree to which repeated measurements under
unchanged conditions show the same results. Although the two words
can be synonymous in colloquial use, they are deliberately
contrasted in the context of the scientific method. A measurement
system can be accurate but not precise, precise but not accurate,
neither, or both. For example, if an experiment contains a
systematic error, then increasing the sample size generally
increases precision but does not improve accuracy. Eliminating the
systematic error improves accuracy but does not change
precision.
The term "predictability" (also called banality) is the degree to
which a correct prediction or forecast of a system's state can be
made either qualitatively or quantitatively. Perfect predictability
implies strict determinism, but lack of predictability does not
necessarily imply lack of determinism. Limitations on
predictability could be caused by factors such as a lack of
information or excessive complexity.
In one embodiment, the relative amounts of one or more biomarkers
present in a first or second sample of a biological fluid are
determined, in part, by executing an algorithm with a programmable
digital computer. The algorithm identifies at least one peak value
in the first mass spectrum and the second mass spectrum. The
algorithm then compares the signal strength of the peak value of
the first mass spectrum to the signal strength of the peak value of
the second mass spectrum of the mass spectrum. The relative signal
strengths are an indication of the amount of the biomarker that is
present in the first and second samples. A standard containing a
known amount of a biomarker can be analyzed as the second sample to
provide better quantify the amount of the biomarker present in the
first sample. In certain embodiments, the identity of the
biomarkers in the first and second sample can also be
determined.
The basal metabolome of undifferentiated hSLCs serve as a
collection of biochemical signatures of functional pathways that
are relevant for sternness and self-renewal. Metabolite profiling
can be conducted on excreted, secreted or consumed or identified
cellular metabolites as opposed to intracellular compounds.
Ultimately, biomarkers discovered in vitro are expected to be
useful for analyzing in vivo biofluids that contain complex
mixtures of extracellular biomolecules. Such biofluids include but
are not limited to serum, whole blood, plasma, sputum,
cerebrospinal fluid, pleural fluid, amniotic fluid, urine and the
like. This is advantageous over invasive procedures such as tissue
biopsies because small molecules in biofluids can be detected
non-invasively (in contrast to intracellular compounds). In
addition, processing cellular supernatant for mass spectrometry is
more robust and less laborious than cellular extracts. However,
cellular extracts (from, for example, lysed cells) can be utilized
in the methods of the invention.
The term "biomarker profile" as used herein refers to a plurality
of biomarkers identified by the inventive methods. Biomarker
profiles according to the invention can provide a molecular
"fingerprint" of the toxic and teratogenic effects of a test
compound and convey what cellular metabolites, specifically
excreted and secreted cellular metabolites, are significantly
altered following test compound administration to hSLCs. In these
embodiments, each of the plurality of biomarkers is characterized
and identified by its unique molecular mass and consistency with
which the biomarker is detected in response to a particular toxic,
teratogenic or test chemical compound; thus the actual identity of
the underlying compound that corresponds to the biomarker is not
required for the practice of this invention.
The term "biomarker portfolio" as used herein refers to a
collection of individual biomarker profiles. The biomarker
portfolios can be used as references to compare biomarker profiles
from novel or unknown compounds. Biomarker portfolios can be used
for identifying common networks, particularly metabolic or
developmental networks, of toxic or teratogenic response.
The results set forth herein demonstrate that hSLC metabolomics can
be used in biomarker discovery and network identification.
Metabolomics detected small molecules secreted or excreted by
hSLCs, consumed by hSLCs, or the flux of metabolites through hSLCs.
The identified biomarkers can be used for at least two purposes:
first, to determine specific metabolic or biochemical networks or
networks that respond to or are affected by toxin or teratogen
exposure, particularly said networks utilized or affected during
early development that are sensitive to toxic, teratogenic or test
chemical compounds that are developmental disruptors and
participate in the ontogenesis of birth defects; and second, to
provide metabolites that can be measured in biofluids to assist
management and diagnosis of toxic exposure, birth defects or other
disease.
In one embodiment, the metabolites of a biomarker portfolio are
mapped to one or more metabolic networks in order to determine key
developmental pathways affected by a test compound. In one aspect,
online databases are used to map the metabolites to one or more
developmental networks. These online databases include, but are not
limited to, HMDB, KEGG, PubChem Compound, and METLIN. In another
embodiment, one or more developmental processes associated with the
one or more metabolic networks are identified in order to determine
one or more developmental processes or pathways disrupted by a test
compound.
In a further embodiment, the potential specific effect of a
teratogenic compound can be identified with further consideration.
Specifically, by way of example, it is known that certain
developmental or biological defects are correlated to disruptions
in one or more metabolic networks, and by not just identifying the
existence of the metabolites affected by the disruption of these
metabolic networks, but further comparing the affected metabolites
to their normal metabolic network profiles, a person of skill in
the art would be able to correlate the specific effect of the
teratogenic compound to its potential specific biological effect on
a patient. This type of information helps to elucidate specific
developmental pathways that may be affected by exposure to a
teratogenic compound.
A biomarker portfolio from hSLCs can also serve as a high
throughput screening tool in preclinical phases of drug discovery.
In addition, this approach can be used to detect detrimental
effects of environmental (heavy metals, industrial waste products)
and nutritional chemicals (such as alcohol) on human development.
Ultimately, the methods of this invention utilizing the hSLC
metabolome can assist pharmaceutical, biotechnology and
environmental agencies on decision-making towards development of
compounds and critical doses for human exposure. The integration of
chemical biology to embryonic stem cell technology also offers
unique opportunities to strengthen understanding of human
development and disease. Metabolomics of cells differentiated from
hSLCs should serve similar roles and be useful for elucidating
mechanisms of toxicity and disease with greater sensitivity for
particular cell or tissue types, and in a human-specific
manner.
For example, key metabolic networks, including as set forth herein
arginine, aspartic acid, gamma aminobutyric acid (GABA), glutamate
and isoleucine synthesis and degradation, may be differentially
disrupted in earlier versus later stages of human development. In
addition, metabolite profiles of neural precursor cells or neuronal
cell populations can reveal biomarkers of neurodevelopmental
disorders in target cell types. The association of metabolomics to
stem cell biology can inform the mechanisms of action of folic acid
and neural tube defects in the early human embryo.
Biomarker portfolios produced using the hSLC-dependent methods of
this invention can also be used in high throughput screening
methods for preclinical assessment of drug candidates and lead
compounds in drug discovery. This aspect of the inventive methods
produces minimal impact on industry resources in comparison to
current developmental toxicology models, since implementation of
this technology does not require experimental animals. The
resulting positive impact on productivity enables research teams in
the pharmaceutical industry to select and advance compounds into
exploratory development with greater confidence and decreased risk
of encountering adverse developmental effects.
The term "developmental pathway" or "developmental process" or
"developmental network" as used herein refers to biochemical or
metabolic networks involved in embryonic and fetal development.
"Supernatant" as used herein can include but is not limited to
extracellular media, co-cultured media, cells, or a solution of
fractionated or lysed cells.
Metabolite profiles obtained from analysis of toxins, teratogens,
alcohol, and test chemical compounds can be used to compose a
library of biomarker portfolios. These portfolios can then be used
as a reference for toxicological analysis of unknown chemical
compounds. Metabolic profiles of novel compounds can be compared to
known biomarker portfolios to identify common mechanisms of toxic
response. This approach can reveal functional markers of toxic
response, which serve as screening molecules that are shared at
least in part as a consequence of exposure to various different
toxic and teratogenic compounds. Such hSLC-derived small molecules
can be used as measurable mediators of toxic response that refine
or replace costly and complex screening systems (such as in vivo
animal models) and have the additional advantage of being specific
for human cells and human metabolic and developmental networks.
Kits
As a matter of convenience, the method of this invention can be
provided in the form of a kit. Such a kit is a packaged combination
comprising the basic elements of: a) a first container comprising,
in solid form, a specific set of purified metabolites having a
molecular weight of less than about 3000 Daltons, wherein a
difference in the specific set of purified metabolites associated
with hSLCs cultured in the presence of known teratogenic compounds
versus hSLCs cultured in the absence of known teratogenic compounds
indicates a difference in metabolic response of hSLCs cultured in
the presence of the known teratogenic compounds in comparison with
hSLCs cultured in the absence the known teratogenic compounds; and
b) a second container comprising a physiologically suitable buffer
for resuspending the specific subset of purified metabolites.
In one embodiment, the kit can further include an instruction
sheet, describing how to carry out the assay of the kit.
In another embodiment, the kit can also encompass one or more
reagents to analyze fluctuations of expression and/or activity of
one or more enzymes which are involved in the endogenous biological
reactions which result in the synthesis and/or conversion of one or
more metabolites disclosed herein. Thus, the kit is not limited to
the analysis and detection of small molecule biomarkers, but also
of the enzymes which are inherent components of the metabolic
networks described herein. In one embodiment, analysis of enzyme
activity and/or concentration in the kit, as an indicator of
metabolite changes can be performed by assays including but not
limited to gene expression analysis, ELISA and other immunoassays
as well as enzyme substrate conversion.
In another embodiment, the invention discloses a method for
validating a test compound as a teratogen. In one embodiment, the
method comprises providing a set of metabolites having a molecular
weight of less than about 3000 Daltons. In one aspect, the
metabolites are provided in the same container. In another aspect,
each metabolite is provided in a separate container. In one aspect,
the metabolites are differentially metabolized by hSLCs cultured in
the presence of one or more known teratogenic compounds in
comparison with hSLCs cultured in the absence of a teratogenic
compound. In one aspect, the metabolites are provided in a solid
form. In another aspect, the metabolites are provided in a liquid
form. Thus, in one embodiment, the method comprises resuspending
the set of metabolites. In one aspect, the metabolites are
resuspended in a buffer. In another aspect, metabolites are
resuspended in any suitable liquid. In another aspect, the buffer
is a physiologically suitable buffer. In one aspect, the
metabolites are resuspended in a predetermined volume of the
buffer. In another aspect, the final concentration of each
metabolite in the buffer is identical to the concentration of that
metabolite associated with hSLCs cultured in the presence of one or
more known teratogenic compounds. In another embodiment, the method
comprises generating a reference profile of the metabolites by
method disclosed herein. In yet another embodiment, the method
comprises comparing a profile of mass features generated upon
exposure of hSLCs to the test compound with the reference profile
of metabolites in order to validate the teratogenicity of the test
compound.
Advantages of a hSLC Developmental Toxicity Prediction Model
The hSLC-based assay reported herein has several distinct
advantages over other standard approaches, namely: 1) Alterations
to the metabolites in response to a toxicant is a sensitive and
quantitative measurement, which enables more objective data-driven
decisions. 2) Multiple biochemical pathways can be assessed
simultaneously, which reinforces the robustness of the model when
applied to drugs with a variety of mechanisms of toxicity. 3)
Metabolic endpoints are a measure of functional biochemical
pathways that can be rapidly integrated with protein, DNA, and RNA
targets for further pathway-based investigation. 4) Because the
prediction is based on multiple independent variables, it is
possible to detect teratogens exhibiting complex changes in
metabolic patterns. 5) The assay is independent of cell death
outcomes and is trained on circulating doses known to cause human
developmental toxicity, which increases the probability of finding
developmental toxicants that are not just toxic to dividing cells.
6) Testing and analysis is higher throughput, less labor intensive
and automatable.
Comparison of hSLC Developmental Toxicity Prediction Model to Other
Models
Developmental toxicity testing in cells derived from human embryos
is highly likely to generate more reliable in vitro prediction
endpoints than those currently available through the use of animal
models, or other in vitro non-human assays such as zebra fish
models, the EST, and whole embryo culture (WEC) given the
physiological relevance of hSLC to human development.
The hSLC model has important biological features in comparison to
zebrafish assay systems. First, it is a human system, providing
species specificity to predict human outcomes. Zebrafish
developmental and biochemical pathways can be quite distinct from
those that are critical to human development, for example the
absence of placentation and pulmonary differentiation and
development, as well as different mechanisms for cardiogenesis.
Moreover, the screening throughput of zebrafish assays is somewhat
limited due to the high degree of developmental defects associated
with small well size (Selderslaghs et al. 2009). The fish are also
sensitive to very low concentrations of DMSO, where levels greater
than 0.25% cause increased deformities. The determination of a
specific defect, by visual inspection of changes in morphology, can
also be highly subjective while perturbation to the abundance of
small molecule metabolites is a quantitative endpoint measured by a
highly sensitive analytical chemistry technique
(LC-ESI-QTOF-MS).
TABLE-US-00001 TABLE 1 Accuracy of Developmental Toxicity Models
Model # Drugs Accuracy Zebra Fish (McGrath 2008) 12 91 devTOX
(hSLCs) 8 88 EST (Paquette 2008) 63 83 WEC (Genschow 2002) 14 80
EST (Genschow 2002) 20 78 Zebra Fish Embryos (Chapin 2008) 18 72 MM
(Genschow 2002) 20 70 WEC (Genschow 2002) 14 68
In comparison to those reported for the EST, which measures
cytotoxicity and the ability of chemicals to disrupt proper
differentiation of mES cells into cardiomyocytes, the overall
reliability of the hSLC assay reported here, based on a metabolic
signature of toxicity, was superior to the EST. The EST predictive
model is strongly correlated with cytotoxicity, given that two EST
variables result from the IC50 concentrations observed in
fibroblasts compared to mES cells. These variables make the
assumption that developmental toxicants cause cell death at lower
concentrations in embryonic cells compared to the "adult"
fibroblast cells, which may not be valid for many mechanisms of
toxicity (for example--Thalidomide). The dose required to reach an
IC50 may also be much higher than the typical circulating dose or
that which may be encountered by the fetus in utero leading to
large numbers of false positives. It is also likely that changes in
cell viability may be observed in vitro which will not occur in
vivo.
Further, the hSLC based assay correctly classifies thalidomide as a
teratogen while the EST does not (Nieden et al. 2001). The hSLC
model is also considerably more predictive than either WEC or micro
mass (MM) (Table 1). Further, the hSLC and metabolomics based model
offers an opportunity to understand the mechanisms of developmental
toxicity in an all human system.
In one embodiment, a virtual library containing all the biomarkers
discovered in this study can be established. Such a library
provides a repository of human biomarkers useful in assessing
developmental toxicity, not only of pharmaceutical agents, but also
of other chemicals, the latter subject to increased attention from
regulatory directives, namely REACH, in Europe. By integrating a
larger number of pharmaceutical compounds in addition to other
chemicals that are known to disrupt human development (such as
chlorpyrifos, organophosphates, methylmercury) one can further
expand the biomarker library and the robustness of metabolomics
biomarkers across very diverse collections of chemicals. Although
exemplified in a six-well format, metabolomics of hSLCs in a
96-well format are contemplated to enable high-throughput screening
of chemical collections such as those available at the Molecular
Libraries Program (NIH) or NTP (National Toxicology Program,
NIEHS). In addition, a targeted metabolomics approach employing the
use of triple quadrupole MS for ultra fast, sensitive and more
specific quantitation of metabolites is expected to improve
throughput.
The present invention illustrates the ability to utilize hSLCs and
metabolomics to provide a predictive, quantitative, all-human in
vitro screening method for predicting developmental toxicity of
compounds. The model also provides the opportunity to investigate
mechanisms of toxicity of compounds by studying the metabolite
response of hSLCs exposed to those compounds. Thus, this method has
the potential to aid in the prevention of birth defects induced by
chemical compounds and to reduce animal testing.
In one embodiment, the present invention provides a more predictive
in vitro assay than those currently available in order to further
identify biomarkers that are specific to humans, rather than to
rodents or other non-human biological systems. Therefore, in one
embodiment, the invention provides assays that are more accurate,
sensitive, and/or specific than available assays.
In one embodiment, the invention discloses a method for predicting
the teratogenicity of a test compound with at least about 80%
accuracy, and more particularly with at least about 85% accuracy.
In preferred embodiments, the invention discloses a method for
predicting the teratogenicity of a test compound with at least
about 90% accuracy.
In another embodiment, the invention discloses a method for
predicting the teratogenicity of a test compound with at least
about 80% sensitivity, more particularly with at least about 85%
sensitivity, and even more particularly with at least about 95%
sensitivity.
In still another embodiment, the invention discloses a method for
predicting the teratogenicity of a test compound with at least
about 80% specificity, and more particularly with at least about
85% specificity. In preferred embodiments, the invention discloses
a method for predicting the teratogenicity of a test compound with
at least about 95% specificity.
In one embodiment, the invention uses a machine learning model to
develop a highly accurate, sensitive, and specific assay to
determine teratogenicity of test compounds. Accordingly, in one
embodiment, the invention provides an initial training set of known
teratogenic and non-teratogenic compounds to dose hSLCs. In another
embodiment, the invention adds a test compound identified as a
teratogen to the initial training set to obtain an expanded
training set. In one embodiment, the expanded training set allows
for a more accurate, sensitive, and specific model for predicting
teratogenicity of test compounds.
In one embodiment, dosing compounds were dosed at concentrations
corresponding to their IC50 or EC50 dose levels. In another
embodiment dosing compounds were dosed at concentrations
corresponding to two doses below their IC50 or EC50 dose levels. In
another embodiment, dosing compounds were dosed at concentrations
corresponding to their circulating dose. In one aspect, dosing
compounds at concentrations corresponding to their circulating dose
recapitulates the exposure level to a developing human embryo in
vivo and the toxic or teratogenic effect of the dosing compound on
human development.
In one embodiment, determination of teratogenicity of a test
compound involves comparing the metabolic response of hSLCs
cultured in the presence of a test compound with the metabolic
response of hSLCs cultured in the absence of the test compound. In
another embodiment, determination of teratogenicity of a test
compound involves comparing the metabolic response of hSLCs
cultured in the presence of a test compound with the metabolic
response of hSLCs cultured in the presence of a known
non-teratogenic compound. In one aspect, the comparison of
metabolic response of hSLCs cultured in the presence of a test
compound with the metabolic response of hSLCs cultured in the
presence of a known non-teratogenic compound allows for a more
specific, sensitive, and accurate assay to predict teratogenicity
of a test compound. In one embodiment, a non-teratogenic compound
is any compound that, upon exposure to hSLCs, does not alter the
normal metabolism of hSLCs. Examples of non-teratogenic compounds
or agents include, but are not limited to, sugars, fatty acids,
spermicides, acetaminophens, prenatal vitamins, and the like.
EXAMPLES
The Examples which follow are illustrative of specific embodiments
of the invention, and various uses thereof. They are set forth for
explanatory purposes only, and are not to be taken as limiting the
invention.
Example 1
hES Cell Culture
WA09 hESCs, obtained from WiCell Research Institute (NIH National
Stem Cell Bank, Madison, Wis.) were cultured in 6-well plates on
Matrigel (BD Biosciences, San Jose, Calif.), in mTeSR1 medium (Stem
Cell Technologies, Vancouver, BC) incubated at 37.degree. C. under
5% CO.sub.2 in a Thermo Electron Form a Series II Water Jacket CO2
Incubator. hESCs were passaged every three or four days at a 1:3 or
1:6 seeding density for routine culture conditions. For dosing
experiments, hESCs were passaged at a low density of 1:10 or 1:12
so that they would not require passaging during the seven-day
dosing protocol. To passage hES cells, the StemPro.RTM.
EZPassage.TM. disposable stem cell passaging tool (Invitrogen,
Carlsbad, Calif.) was used to detach the cells from the wells.
Detached cells were removed with a pipette and distributed to new
Matrigel plates.
Example 2
hES Cell Dosing
A training set of established teratogens and non-teratogens (Table
2) was used to dose hESCs. The training set is a collection of
chemical standards that includes compounds that had been previously
used in multicenter efforts aimed at developing and validating
novel alternatives to predict developmental toxicity, such as the
EST, proposed by the ECVAM agency.
All tested chemicals were purchased from Sigma-Aldrich (St. Louis,
Mo.). Cells were dosed with drugs at a concentration equivalent to
their published serum circulating therapeutic dosages. Dosing was
performed on hESCs in 6-well plates in triplicate, i.e. three wells
per plate. The plates were dosed in triplicate, so there were a
total of nine dosed wells. In parallel, there were nine "control"
wells, in which hESCs were cultured with mTeSR1 containing no drug,
and three wells containing Matrigel with mTeSR1 medium without
hESCs that served as medium controls. Lastly, three wells of dosed
medium controls were prepared, containing Matrigel, mTeSR1 and
drug, but no hESCs (FIG. 1). These medium controls provided
baseline mass spectral data. On the first day of dosage, the
determined concentration of drug was dissolved in mTeSR1, and then
2.5 mL of this solution was added to each dosed well of hESCs. Each
day, for four days, the medium was removed and new dosed medium was
added. On the fourth day, the medium was removed and added to
acetonitrile to make a 40% acetonitrile solution, as outlined in
the Sample Preparation section below.
Since it is the goal of the present study to develop a more
predictive in vitro assay than those currently available, and to
further identify biomarkers that are specific to humans, rather
than to rodents or other non-human biological systems, the ECVAM
test set was replicated in this study. Additional drugs were
included in the training set to increase the number of the
non-teratogen chemicals, as well as to supplement the strong
teratogens.
TABLE-US-00002 TABLE 2 Chemical compounds in the training and test
set (blinds) used for dosing, their classification according to
teratogenicity and prediction model incorporation. TS1 and TS2
indicate Training Set 1 and 2 respectively. Model Stemina Training
ECVAM Classification Compound Set Classification Non-Teratogens
Ascorbic Acid TS1, 2 Non-Teratogens Doxylamine (Blind 2) TS2
Isoniazid TS1, 2 Levothyroxine TS1, 2 Penicillin G TS1, 2 Folic
Acid TS1, 2 Retinol (Blind 1) TS2 Thiamine (Blind 8) TS2 Aspirin
TS2 Weak/Moderate Caffeine TS2 Teratogens Dexamethasone
Diphenhydramine TS2 Teratogens Diphenylhydantoin TS2 Methotrexate
TS2 5-Fluorouracil TS1, 2 Strong Teratogens Accutane (Blind 6) TS2
Amiodarone (Blind 3) TS2 Busulfan TS1, 2 Carbamazepine TS2 (Blind
5) Cyclophosphamide TS2 (Blind 7) Cytosine Arabinoside TS1, 2
Hydroxyurea TS1, 2 Retinoic Acid TS1, 2 Rifampicin (Blind 4) TS2
Thalidomide TS1, 2 Valproic Acid TS1, 2
Compounds were dosed at concentrations corresponding to their
circulating dose rather than IC50 or EC50 dose levels. Dosing was
done at the circulating maternal dose as published in the
literature in an effort to recapitulate the exposure level to the
developing human embryo in vivo and the toxic effect on human
development rather than creating a model which measures toxic
effect on hESCs in culture. It is noteworthy to mention that the
substances employed in this screen (the ECVAM test set) exert their
developmental toxicity in a manner that is independent of maternal
metabolism.
In other words, this test set was established and employed in
multicenter, randomized trials due to the fact that the parent
compound, and not reactive metabolites, impair proper human
development and are thus suitable to develop novel means for in
vitro screening.
Example 3
hES Cell Viability Assays
In addition to determining teratogenicity by molecular endpoints,
with metabolomics, cell viability was examined using a subset of
the drugs to determine if a correlation exists between cell death
and compound teratogenicity. In particular, the viability assay was
conducted to address the concern that the metabolic endpoints may
be strongly correlated with cell death rather than developmental
toxicity since dosing with the antineoplastic drugs cytosine
arabinoside and 5-fluorouracil often resulted in the most profound
changes in many metabolites.
Cell viability assessment in response to exposure to chemical
compounds was examined using the MultiTox-Fluor Assay (Promega,
Madison, Wis.), which simultaneously measures cell viability and
cytotoxicity. WA09 hESCs were seeded at a density of 250,000
cells/well in a 96-well plate. Cells were fed with dosed media
daily, for four days. On the fourth day, spent medium was removed,
100 .mu.L of fresh medium was added along with 100 .mu.L of the
MultiTox-Fluor reagent. The plate was incubated at 37.degree. C, 5%
CO2 for 30 minutes and measured. The ratios of live to dead cells
were normalized to the control cells (no treatment) in order to
report relative cell viability.
Cell viability data (FIG. 2) showed no discernable correlation
between teratogenicity and cell death relative to control cells.
Thus, therapeutic concentrations of teratogens are not correlated
with cell death in a significant manner, despite the evidence of
statistically significant metabolomic changes. This finding
suggests that metabolomics has a lower threshold, or increased
sensitivity to detect molecular changes associated with
developmental toxicity and specific biomarkers in comparison to
standard cell death assays, which should provide a more predictive
and sensitive screen for developmental toxicity.
Example 4
Developmental Toxicology Screening
Sample Preparation
The 2.5 mL of spent media per well from Example 1 was added to 1.67
mL acetonitrile to make a 40% acetonitrile solution. The
acetonitrile acts to "quench" the spent media sample, slowing or
halting many metabolic processes and aiding in precipitation of
cellular proteins. Samples were either stored at -80.degree. C. for
later analysis, or for immediate analysis, 250 .mu.L of the
quenched solution was mixed with 250 .mu.L of water, to a final
concentration of 20% acetonitrile, then added to a 3 kDa molecular
weight cut-off filter spin column (Microcon YM-3 Centrifugal
Filter, Millipore, Billerica, Mass.). Each sample was then
centrifuged in an IEC CL31R Multispeed Centrifuge (Thermo
Scientific, Waltham, Mass.) at 13,000.times.g at 4.degree. C. for
200 minutes. Following centrifugation, the flow-through was saved
then dried for several hours in a Savant High Capacity Speedvac
Plus Concentrator. The concentrated sample was then dissolved in 50
.mu.L of 0.1% formic acid prior to LC-MS analysis.
Mass Spectrometry
Mass spectrometry was performed using an Agilent QTOF LC/MS system
consisting of a G6520AA QTOF high resolution mass spectrometer
capable of exact mass MS and MS/MS. In order to facilitate
separation of small molecules with a wide range of polarity and to
allow increased retention of hydrophilic species, Hydrophilic
Interaction Liquid Chromatography (Alpert 1990) was employed. Each
sample was run for 30 minutes with the gradient shown in Table 3 at
a flow rate of 0.5 mL/min, using 0.1% formic acid in water (Solvent
A) and 0.1% formic acid in acetonitrile (Solvent B). Electrospray
ionization was employed using a dual ESI source, with an Agilent
isocratic pump continuously delivering an internal mass reference
solution into the source at approx. 0.01 mL/min. The mass range of
the instrument was set to 100-1700 Da. A Phenomenex Luna HILIC
column with dimensions 3.times.100 mm 3 .mu.m particle size was
used and maintained at 30.degree. C. 5 .mu.l of each sample was
injected. Data acquisition was performed with Agilent MassHunter
using high-resolution exact mass conditions.
TABLE-US-00003 TABLE 3 HILIC gradient % B % A Acetonitrile with
0.1% Time (min) 0.1% formic acid (aq) formic acid 0.0 5 95 1.5 5 95
16.0 40 60 17.0 95 5 21.0 95 5 22.0 5 95 30.0 5 95
Mass Spectral Data Preprocessing
Following LC-MS, chromatograms were inspected for reproducibility.
LC-MS runs with total ion counts that vary by more than 25% were
repeated to ensure that samples could be accurately compared. These
runs were then used to create mass features that correspond to
molecules detected across the different LC-MS runs. Mass features
were extracted from the LC-MS data using MassHunter Qualitative
Analysis software (Agilent Technologies). The following criteria
were used as general guidelines, however some flexibility and
optimization was needed. m/z values within the range of 75-1500,
with a charge of +1 or -1, and a centroid height greater than 1000
were used to generate "mass features." The mass peaks that pass
these criteria were used to fit isotope and adduct (Na.sup.+,
K.sup.+, and NH4.sup.+) patterns corresponding to individual
molecules, and to establish the abundance of each mass feature. The
abundance is calculated by MassHunter software as the sum of the
isotopic and adduct peaks that correspond to a single molecular
feature. After data deconvolution, mass features showing at least
two ions (e.g. (M.sup.+H).sup.+ and (M.sup.+H).sup.++1 or
(M.sup.+H).sup.+ and (M.sup.+Na).sup.+) and an abundance value
greater than 50000 for positive-ion mode data and 10000 in
negative-ion mode data were included in the data set used for
binning of the mass features.
Following feature selection by MassHunter, the data was further
preprocessed by MassProfiler (Agilent) software which aligns mass
features across multiple LC-MS data files. Mass features were
generated for data from each drug treatment experiment (dosed and
control) using the default alignment settings in MassProfiler with
the requirement that a feature be present in at least 80% of the
samples in one treatment. The mass feature datasets for each drug
treatment experiment were further processed in a global manner
using custom analysis scripts executed in the R statistical
software environment.
Files for each drug experiment were binned using an algorithm based
on both exact mass and retention time in order to consider a mass
feature the same across different LC/ESI-MS-QTOF runs. The binning
criteria is based on both a sliding mass difference scale that
allows for larger mass differences at lower molecular weights and a
constant retention time window based on the reproducibility of the
chromatography. Masses were ordered and considered to be the same
feature if a mass under 175 Da differs by less than 18 ppm from the
previous mass, while masses 176-300 Da were binned by 12 ppm, and
10 ppm when over 300 Da. These mass bins were ordered by retention
time and if a difference in retention of the previous feature was
less than twelve seconds it was considered to be the same feature
across LC-MS runs. The binning process is used to create unique
compound identities (cpdID) that are assumed to represent a single
small molecule. If multiple features appeared to fall into the same
bin their abundances were averaged.
Determination of Metabolic Flux, Secreted, Excreted, Consumed, or
Identified Metabolites
The media represents a major factor in the experimental system, in
that it contributes many peaks to mass spectra. This can be
accounted for in a data dependent manner to select for mass feature
bins, which are present at significant levels above the media. Mass
feature bins present solely in the presence of cells (not detected
in media) or with average abundance levels different than
uncultured media were considered to be secreted, excreted,
consumed, or identified metabolites.
Validation of Small Molecule Metabolites
In validating the identities of specific metabolites, three
criteria were used: 1)
The exact mass of the metabolite must be within 10 ppm of the known
mass of the compound. 2) The retention time of the metabolite
detected in the cell media must be within + or -30 seconds of a
reference standard on which MS data had been acquired under the
same conditions. The reference standards were dissolved in mTeSR
media and prepared in exactly the same manner (described above) as
the samples from the cells, including the addition of acetonitrile,
Centricon centrifugal filtration, drying then dissolution in formic
acid prior to LC-MS analysis. 3) The MS-MS fragmentation spectra of
the metabolite detected in the cell media must be a reasonable
match with that of the reference standard, including abundances and
m/z values of the fragment ions. If published MS-MS spectra are
available, the MS-MS spectra must also be a reasonable match.
Example 5
The Random Forest Model
Teratogen Classification for the Random Forest Model
The classification of teratogenicity in previously published animal
and cell culture models of developmental toxicity were trained
using three different classes, non-teratogens, weak/moderate
teratogens, and strong teratogens, based largely on embryotoxicity
outcomes and developmental abnormalities observed in animal models
(Marx-Stoelting et al. 2009, Chapin et al. 2008). In the present
study a modified approach to compound classification since there
are many species specific differences in developmental toxicity,
focusing the compound teratogenicity classification strictly on
observed human risk associated with each chemical.
Thus, the criteria of observed human teratogenicity risk led to a
model with two categories of toxicity, teratogen or non-teratogen,
which accurately reflects the ultimate intended outcome of the
predictive model. This also reduces technical challenges associated
with attempting to determine the potency of teratogens based on
distantly related species. Additionally, such a focused
classification schema (teratogens versus non-teratogens) leads to a
more robust and predictive metabolic model of human developmental
toxicity given the limited availability of reliable, quantitative
data of human risk associated with exposure to weak or moderate
teratogens.
Random Forest Modeling
Random Forest (Brieman 2001) was used to create a classification
model in order to predict teratogenicity and non-teratogenicity
using the median fold change of drug treatment versus its
intra-experimental control for each feature (variable) included in
the model. Bagging was performed on 1/3 of the samples by
re-sampling with replacement 1000 bootstrap subsets from the
training set data of known teratogens. Final prediction from the RF
classifier on the blinded drugs was based on the majority vote of
the ensemble of trees.
Feature Set Used for Random Forest Modeling
The dataset utilized for random forest modeling was a subset of
high quality reproducible features. Features were selected if they
had values present in at least 75% of the drug treatment
experiments (blind and known drugs). This list of features was then
filtered against a list of known contaminant molecules such as
HEPES and PEG and their numerous adducts to remove features of
non-biological interest. Finally, features with poor binning or
grouping characteristics were removed.
Feature selection by variable importance was performed by selecting
features with a mean decrease in accuracy greater than 0.5. Random
forest based analysis was executed using the Random Forest library
(Liaw & Wiener 2002). Model metrics were calculated based on
the resulting random forest confusion matrix or the predictions of
blinded drugs using the methods outlined in (Genschow et al.
2000).
The abundance values were then log base two transformed and the
median value of each treatment (dosed and control) within each
experiment (different drugs) was used. The data was then normalized
by control for each drug treatment experiment. The resulting median
log fold change values were used as the input data values for the
random forest modeling. Missing median fold change data was
replaced with a 0. The remaining positive and negative ESI mode
features were combined creating a dataset with 142 features used
for modeling.
Example 6
Random Forest Model Results
As discussed in Example 5, random forest model was trained using a
filtered dataset consisting of reproducibly measured mass features
from both ESI polarities. The median fold change value of a mass
feature for the replicates for each drug versus its associated
intra-experimental control were used as the variables to predict
the teratogenicity of drugs.
The initial training set (TS1) contained 142 mass features
resulting from exposure of hESCs to seven teratogens and five
non-teratogens (see Table 2).
The detailed annotations for the 142 mass features is provided in
Table 4. In comparing the retention times (RT) and mass averages
(MASSavg) of each of the mass features with masses recorded in
databases such as Kegg, Metlin, HMDB, CAS, PUBCHEMS, PUBCHEMC,
CHEBI etc., one will find typically one or more putative candidate
metabolites for each mass feature that match the retention times
and mass averages. During the subsequent validation process, the
identity of the metabolite corresponding to a specific retention
time and mass average is determined. The metabolite identities
validated thus far are also provided in Table 4.
TABLE-US-00004 TABLE 4 Feature Table Summary newID ESImode RT
MASSavg Metabolite NEGM102T150 NEG 150 102.0317 neg11 neg 443
103.0631 Gamma- Aminobutyric acid neg12 neg 78 104.0473 neg15 neg
504 105.9670 NEGM116T150 NEG 150 116.0110 NEGM116T90 NEG 90
116.0110 Fumaric acid neg71 neg 81 118.0265 Succinic acid neg73 neg
76 118.0626 Succinic acid neg94 neg 655 121.0202 neg101 neg 103
129.0427 neg105 neg 103 129.1420 neg133 neg 75 132.0779
Hydroxyisocaproic acid ESIneg.M132T451 NEG 451 133.0375 Aspartic
Acid NEGM134T120 NEG 120 134.0215 Malic acid neg158 neg 71 139.0626
NEGM147T450 NEG 450 147.0532 L-Glutamic acid neg198 neg 503
149.9571 NEGM155T288 NEG 288 155.0695 neg275 neg 441 169.0339
neg295 neg 320 173.0816 NEGM174T505 NEG 505 174.1117 L-Arginine
neg360 neg 438 187.0445 neg360 NEG 445 187.0453 neg414 neg 496
200.0279 neg429 neg 436 203.0548 neg431 neg 80 203.1149 neg435 neg
506 204.0192 ESIneg.M215T293 NEG 293 216.0391 neg559 neg 445
231.0737 neg563 neg 73 231.9462 neg622 neg 655 240.0229 L-Cystine
neg763 neg 441 260.0964 neg776 neg 655 262.0048 neg779 neg 504
263.9399 neg811 neg 320 267.0687 neg831 neg 435 271.0422 neg840 neg
502 273.9691 neg1095 neg 73 325.9324 neg1112 neg 495 328.0276
neg1121 neg 55 331.0842 neg1136 neg 90 333.1022 neg1149 neg 494
336.0678 neg1167 neg 435 339.0297 neg1192 neg 493 346.0948 neg1264
neg 487 360.1113 neg1366 neg 441 379.1370 neg1433 neg 448 393.1215
neg1458 neg 73 401.8984 neg1491 neg 434 407.0167 neg1568 neg 82
426.0714 neg1700 neg 83 453.2425 neg1728 neg 485 460.1752 neg1787
neg 434 475.0041 neg1932 neg 654 502.0281 neg2068 neg 482 528.1597
neg2115 neg 434 542.9914 neg2355 neg 434 610.9786 neg2606 neg 434
678.9659 neg3446 neg 88 1051.0609 neg3535 neg 73 1110.9571 pos48
pos 55 103.2278 pos69 pos 661 105.9789 pos102 pos 80 113.0843
pos102 POS 81 113.0844 pos134 pos 345 117.0787 L-Valine pos136 pos
54 117.1150 pos213 pos 43 129.1513 POSM131T330 POS 330 131.0946
L-Isoleucine pos368 pos 90 150.0898 PEG (n = 3) POSM155T288 POS 288
155.0695 pos422 pos 83 156.1256 pos446 pos 79 158.1412 pos477 pos
439 164.0466 pos518 pos 79 169.0740 Pyridoxine pos525 pos 89
170.0574 pos529 pos 72 170.1416 POSM174T505 POS 505 174.1117
L-Arginine pos563 pos 503 174.2275 pos593 pos 44 177.1264 pos625
pos 511 181.9569 pos628 pos 42 182.1780 pos681 pos 133 188.1885
pos687 pos 46 189.1261 pos687 POS 48 189.1264 pos698 pos 441
191.0167 pos744 pos 70 198.1728 POSM202T432 POS 432 202.1430
Asymmetric Dimethyl-L- arginine pos892 pos 88 219.1119 Pantothenic
Acid pos917 pos 640 222.0666 L-Cystathionine pos922 pos 511
222.9821 pos962 pos 503 227.9989 pos970 pos 43 229.2402 pos1062 pos
655 240.0245 pos1084 pos 50 242.1753 pos1095 pos 96 244.0927
pos1113 pos 493 246.0704 ESIpos.M286T667 POS 667 285.0961 pos1471
pos 98 288.1188 pos1668 pos 437 312.0289 pos1684 pos 485 314.1091
pos1698 pos 42 315.2042 pos1698 POS 47 315.2050 pos1734 pos 597
320.0177 pos1773 pos 484 325.5943 pos1791 pos 506 328.0611 pos1820
pos 600 331.8777 pos1896 pos 495 342.1509 pos1975 pos 257 354.0566
Phenol Red pos2019 pos 434 361.0133 pos2094 pos 483 372.1079
pos2109 pos 498 374.0676 pos2178 pos 519 384.2048 pos2225 pos 438
393.0924 pos2284 pos 462 401.2065 pos2288 pos 688 402.0769 pos2489
pos 258 429.9682 pos2512 pos 69 434.1609 pos2527 pos 82 436.2287
pos2634 pos 83 452.2026 pos2693 pos 67 462.1915 pos2763 pos 771
474.0917 pos2786 pos 81 476.2248 pos2814 pos 655 478.1241 pos2823
pos 80 480.2554 pos3023 pos 495 510.1957 pos3095 pos 83 524.2812
pos3183 pos 82 540.2552 pos3308 pos 82 564.2780 pos3425 pos 83
584.2815 pos3627 pos 83 624.2771 pos3675 pos 77 633.3299 pos3728
pos 91 640.8206 pos3777 pos 82 652.3312 pos3795 pos 492 656.2567
pos3832 pos 92 662.8337 pos3871 pos 77 669.3682 pos3912 pos 625
677.2845 pos3978 pos 91 692.3328 pos3980 pos 91 692.8341 pos4039
pos 93 706.3583 pos4063 pos 83 712.3292 pos4079 pos 91 714.8472
pos4136 pos 93 728.3714 pos4178 pos 91 736.3593 pos4208 pos 81
740.3842 pos4250 pos 91 750.3846 pos4267 pos 83 756.3554 pos4283
pos 91 758.8738 pos4340 pos 91 772.3978 pos4343 pos 89 772.8994
pos4371 pos 91 780.3856 pos4435 pos 90 794.4109 pos4438 pos 90
794.9130 pos4513 pos 89 816.4240 pos4515 pos 90 816.9258 pos4543
pos 91 824.4121 pos4559 pos 79 828.4363 pos4594 pos 90 838.4372
pos4596 pos 90 838.9392 pos4617 pos 83 844.4077 newID: Stemina in
house name for mass feature. This is a designation for each
metabolite/mass feature produced during analysis. It designates a
unique feature; ESImode: electrospray ionization mode feature was
detected in; RT: average retention as measured across ~1000 LC-MS
runs of mass feature; MASSavg: average neutral mass as measured
across ~1000 LC-MS runs of mass feature; Metabolite: identity of
the validated metabolite.
These mass features served as the basis for the model that was
applied to predict the teratogenicity of chemical compounds in the
blind studies and treatments. This model was able to correctly
predict the teratogenicity of seven of eight blinded drug
treatments, with a specificity of 100% and sensitivity of 80% and
overall accuracy of 88% (Table 5).
TABLE-US-00005 TABLE 5 Results of the blind study where the
teratogenicity was correctly predicted for 7 of 8 drugs using a
random forest statistical model. Blind # Drug Actual Predicted B1
Retinol Non Non B2 Doxylamine Non Non B3 Amiodarone Ter Ter B4
Rifampicin Ter Ter B5 Carbamazepine Ter Ter B6 Accutane Ter Non B7
Cyclophosphamide Ter Ter B8 Vitamin B1 Non Non
The random forest model was further refined by integrating outcomes
from blinded drugs into the model as known classifiers thereby
increasing the number of non-teratogens and teratogens in the
model, so that the training set consisted of 26 drug treatment
experiments. Feature selection based on the variable importance
measure mean decrease in accuracy resulted in 18 features that were
evaluated as a future predictive model. As a result, the overall
accuracy of the model was ultimately increased to 92% (Table 6),
i.e. the model was able to correctly predict 24 of the 26 drugs
used in the training set. The model was clearly able to
differentiate teratogens from non-teratogens into distinct clusters
when evaluated by multidimensional scaling (FIG. 3) which reflects
clear differences in metabolomics endpoints between treatment
classes.
TABLE-US-00006 TABLE 6 Model metrics for the 18 feature set
prediction model Accuracy Specificity Sensitivity Teratogens
Non-Teratogens Overall 100 87 87 100 92
Following prediction of the blinds, a new model was created by
incorporating the revealed blinds and more drugs into the training
set (TS2, see Table 2). Evaluation of the receiver operating
characteristic (ROC) curve of the model's performance demonstrated
that the model performs in a robust manner (FIG. 4).
Thus, this model shows superior potential for future prediction of
human developmental toxicity in comparison to currently available
assays, and that using iterative modeling as more experiments are
performed is a powerful benefit to the adoption of meaningful
metabolic endpoints in a screen. The predictive ability of this
model is subject to continuous monitoring in response to additional
blinded drug treatments.
Statistically significant differences in the abundance of specific
metabolites were detected in drug-treated and control samples. One
such molecule, asymmetric dimethylarginine (ADMA), exhibited a
significant fold decrease in its abundance in response to valproic
acid treatment exhibiting similar changes for the strong
teratogens: cytosine arabinoside, 5-fluorouracil, hydroxyurea,
amiodarone and cyclophosphamide. ADMA is an inhibitor of nitric
oxide synthase (NOS), an enzyme that converts L-arginine to
L-citrulline which is necessary for neural tube closure (FIG.
5).
Valproate is known to cause neural tube defects (DiLiberti et al.
1984) while nitric oxide synthase activity is essential for neural
tube closure (Nachmany et al. 2006). The novel alterations in the
secretion of dimethylarginine, detected here, suggest that it can
be an appropriate candidate biomarker for neural tube defects.
Arginine levels were also monitored in our data and usually showed
opposite fold changes to those of dimethylarginine in response to
several strong teratogens. To quantify the perturbation of arginine
and ADMA in the hESCs as a result of dosing, EICs (Extracted Ion
Chromatograms) for these compounds were constructed and integrated,
then the ratio of the resulting areas for controls vs. dosed were
compared. These results indicate that the amount of perturbation
may be directly related to the teratogenicity of the dosing
compound. There are no false negatives resulting from these
metrics, and only ascorbic acid and caffeine are false positives
for teratogenicity (Table 7).
TABLE-US-00007 TABLE 7 Selected fold change ratios for arginine and
dimethylarginine. EICs for these compounds were integrated, then
the fold change of the resulting areas for controls vs. dosed were
compared. Smaller fold change ratios (between 0.9 and 1.1) show a
good correlation with non-teratogens, while greater changes
(<0.9 and >1.1) correlate with teratogens. There are no false
negatives for teratogenicity resulting from these metrics and only
ascorbic acid and caffeine are false positives. Arg fold change/
Stemina ADMA fold Arg/ADMA Classification Compound change
Prediction Non-Teratogens Ascorbic Acid 1.28 Ter Aspirin 1.07 Non
Caffeine 1.33 Ter Doxylamine (Blind 2) 0.97 Non Isoniazid 0.94 Non
Levothyroxine 1.03 Non Penicillin G 0.96 Non Folic Acid 1.08 Non
Retinol (Blind 1) 1.03 Non Thiamine (Blind 8) 1.00 Non Teratogens
5-Fluorouracil 43.93 Ter Methotrexate 2.54 Ter Accutane (Blind 6)
0.55 Ter Amiodarone (Blind 3) 1.64 Ter Busulfan 1.12 Ter
Carbamazepine 1.12 Ter (Blind 5) Cyclophosphamide 1.56 Ter (Blind
7) Cytosine Arabinoside 67.01 Ter Hydroxyurea 2.52 Ter Retinoic
Acid 0.48 Ter Rifampicin (Blind 4) 0.81 Ter Thalidomide 0.85 Ter
Valproic Acid 2.11 Ter
Several metabolites that contributed to the random forest
prediction model (PM) were further identified and subject to
chemical identity validation by MS-MS. These include succinic acid,
which shows significant down regulation in its abundance in
response to several teratogens such as carbamazepine,
cyclophosphamide, cytosine arabinoside, 5-fluorouracil,
hydroxyurea, methotrexate, and valproic acid. Other small molecules
that can contribute to the PM are: gamma-aminobutyric acid (GABA),
isoleucine, aspartic acid, malic acid, glutamic acid, and
histidine. These small molecules were significantly altered,
according to the teratogenicity of the test compound and are
correlated to each other on the basis of the biochemical pathways
where they serve as intermediates. This is illustrated in FIG.
6.
For example, aspartic acid, dimethylarginine, and arginine are
components of the urea cycle. This cycle facilitates the removal of
dangerous ammonia through conversion of it to urea, which is
excreted from the body. Succinic acid, isoleucine, and malate are
part of the citric acid cycle, which produces energy for cellular
function. Both networks are linked by glutamate and GABA, which in
turn has a critical role in neuronal physiology.
Certain reactions in the urea cycle take place in the mitochondria,
while the Kreb's cycle is active in the mitochondria in its
entirety. Perturbations to the urea cycle can result in excess
ammonia, which, among a vast array of pathological effects, has
been correlated to newborn deaths (Summar 2001). Interruption of
citric acid cycle reactions compromises cellular energy metabolism
with direct detrimental effects to cellular viability.
Increased concentrations of GABA were detected in the secretome of
hESCs dosed with busulfan, among other teratogens. Dysfunctions in
GABA, underlie well established neurological disorders such as
epilepsy, language delay, and neurodevelopmental impairment, among
others (Pearl & Bigson 2004). The neurodevelopmental toxicity
of busulfan has been previously reported in humans; specifically in
utero exposure to busulfan led to a spinal birth defect due to
insufficient neural fold development, although the mechanism was
not defined (Abramovici et al. 2005).
Example 7
Mechanistic Pathways of Developmental Toxicity
Altogether, metabolomics of hESCs detected statistically
significant alterations to multiple small molecule metabolites
which play a key role in cellular physiology and human development.
Several of these candidate biomarkers were further validated by
MS-MS mass spectrometry in order to confirm their chemical
identity. Significantly, despite the unsupervised nature of the
analysis, many of these significant and validated small molecule
metabolites participate in pathways that had been previously
suggested to underlie developmental toxicity albeit not in cells
derived from human embryos. A list of validated small molecules and
the metabolic networks they map to is provided in Table 8.
TABLE-US-00008 TABLE 8 Small Molecules and Metabolic Networks.
METLIN KEGG HMDB CAS PUBCHEM CHEBI com- com- com- com- Com- Com-
KEGG Metabolic pound pound pound pound pound pound Pathway Network
or Name Formula Mass ID ID ID ID ID ID ID (Function) 2-Hydroxy-
C2H6O4S 125.9987 6987 C05123 HMDB 107-36-8 7866 hsa00430 Tauri- ne
and ethane- 03903 hypotaurine sulfonate metabolism (isethionate)
(sometimes misspelled as isothionate) Cysteic acid C3H7NO5S
169.0045 332 C00506 HMDB 13100-82-8 25701 17285 hsa0- 0270 Cysteine
and (cysteate) 02757 methionine metabolism hsa00430 Taurine and
hypotaurine metabolism hsa04080 Neuroactive ligand- receptor
interaction L- C7H14N2O4S 222.0674 39 C02291 HMDB 56-88-2 439258
17482 hsa00260 Glycin- e, Cysta- 00099 serine and thionine
threonine metabolism hsa00270 Cysteine and methionine metabolism
N1-Acetyl- C9H21N3O 187.1685 3323 C00612 HMDB 34450-16-3 496 17927
(cell growth spermidine 01276 and differentiation) Glycero-
C8H21NO6P 258.1106 370 C00670 HMDB 28319-77-9 439285 16870 hsa005-
64 Glycero- phospho- 00086 phospholipid choline metabolism hsa00565
Ether lipid metabolism Spermine C10H26N4 202.2157 255 C00750 HMDB
71-44-3 1103 15746 hsa00330 Arg- inine 01256 and proline metabolism
hsa00410 beta- Alanine metabolism hsa00480 Glutathione metabolism
Spermidine C7H19N3 145.1579 254 C00315 HMDB 124-20-9 1102 16610
hsa00330 A- rginine 01257 and proline metabolism hsa00410 beta-
Alanine metabolism hsa00480 Glutathione metabolism hsa02010 ABC
transporters 1- C7H9N2O 137.0715 274 C02918 HMDB 3106-60-3 457
16797 hsa00760 Nicotinat- e Methyl- 00699 and nicotinamide
nicotinamide metabolism Nicotinamide C6H6N2O 122.048 1497 C00153
HMDB 98-92-0 936 17154 hsa00760 N- icotinate 01406 and nicotinamide
metabolism L- C9H18NO4 204.1236 956 C02571 HMDB 3040-38-8 18230
15960 (facilitates Acetyl- 00201 movement of carnitine acetyl CoA
into the matrices of mammalian mitochondria) Serotonin C10H12N2O
176.095 74 C00780 HMDB 50-67-9 5202 28790 hsa00380 Try- ptophan
00259 metabolism hsa04080 Neuroactive ligand- receptor interaction
hsa04540 Gap junction Melatonin C13H16N2O2 232.1212 73 C01598 HMDB
73-31-4 896 16796 hsa00380 Tr- yptophan 01389 metabolism hsa04080
Neuroactive ligand- receptor interaction Glutathione C10H17N3O6S
307.0838 44 C00051 HMDB 70-18-8 124886 16856 hsa00- 270 Cysteine
00125 and methionine metabolism hsa00480 Glutathione metabolism
L-Malic C4H6O5 134.0215 118 C00149 HMDB 97-67-6 222656 30797
hsa00020 Citr- ate acid 00156 cycle (TCA cycle) hsa00620 Pyruvate
metabolism hsa00630 Glyoxylate and dicarboxylate metabolism
hsa05200 Pathways in cancer hsa05211 Renal cell carcinoma Maleic
C4H4O4 116.011 4198 C01384 HMDB 110-16-7 444266 18300 hsa00650
Buta- noate acid 00176 metabolism hsa00760 Nicotinate and
nicotinamide metabolism Pyridoxine C8H11NO3 169.0739 2202 C00314
HMDB 65-23-6 1054 16709 hsa00750 - Vitamin B6 00239 metabolism L-
C6H9N3O2 155.0695 21 C00135 HMDB 71-00-1 6274 15971 hsa00340
Histidine Histidine 00177 metabolism hsa00410 beta- Alanine
metabolism hsa00970 Aminoacyl- tRNA biosynthesis hsa02010 ABC
transporters Succinic C4H6O4 118.0266 114 C00042 HMDB 110-15-6 1110
15741 hsa00020 Citr- ate acid 00254 cycle (TCA cycle) hsa00190
Oxidative phosphoryla- tion hsa00250 Alanine, aspartate and
glutamate metabolism hsa00350 Tyrosine metabolism hsa00360
Phenylalanine metabolism hsa00630 Glyoxylate and dicarboxylate
metabolism hsa00640 Propanoate metabolism hsa00650 Butanoate
metabolism L- C6H14N4O2 174.1117 13 C00062 HMDB 74-79-3 6322 16467
hsa00330 Arginine Arginine 00517 and proline metabolism hsa00472
D-Arginine and D-ornithine metabolism hsa00970 Aminoacyl- tRNA
biosynthesis hsa02010 ABC transporters hsa05014 Amyotrophic lateral
sclerosis (ALS) Asymmetric C8H18N4O2 202.143 6309 C03626 HMDB
102783-24-4 123831 17929 (I- nhibitor of Dimethyl- 01539 Nitric
Oxide L-arginine Synthase in Arginine and proline metabolism)
L-Cystine C6H12N2O4S2 240.0239 17 C00491 HMDB hsa00270 Cysteine
00192 and methionine metabolism hsa02010 ABC transporters L-
C6H13NO2 131.0946 23 C00407 HMDB 73-32-5 791 17191 hsa00280 Valine,
Isoleucine 00172 leucine and isoleucine degradation hsa00290
Valine, leucine and isoleucine biosynthesis hsa00970 Aminoacyl-
tRNA biosynthesis hsa02010 ABC transporters Aspartic C4H7NO4
133.0375 15 C00049 HMDB 56-84-8 5960 17053 hsa00250 Alani- ne, Acid
00191 aspartate and glutamate metabolism hsa00260 Glycine, serine
and threonine metabolism hsa00270 Cysteine and methionine
metabolism hsa00300 Lysine biosynthesis hsa00330 Arginine and
proline metabolism hsa00340 Histidine metabolism hsa00410 beta-
Alanine metabolism hsa00460 Cyanoamino acid metabolism hsa00760
Nicotinate and nicotinamide metabolism hsa00770 Pantothenate and
CoA biosynthesis hsa00910 Nitrogen metabolism hsa00970 Aminoacyl-
tRNA biosynthesis hsa02010 ABC transporters hsa04080 Neuroactive
ligand- receptor interaction Gamma- C4H9NO2 103.0633 279 C00334
HMDB 56-12-2 119 16865 hsa00250 Alanine-
, Amino- 00112 aspartate butyric and acid glutamate (GABA)
metabolism hsa00330 Arginine and proline metabolism hsa00410 beta-
Alanine metabolism hsa00650 Butanoate metabolism hsa04080
Neuroactive ligand- receptor interaction Mevalonic C6H12O4 148.0736
127 C00418 HMDB 150-97-0 439230 17710 hsa00900 - Terpenoid acid
00227 backbone biosynthesis 2'- C9H12N2O5 228.0746 91 C00526 HMDB
951-78-0 13712 16450 hsa00240 Pyrimi- dine deoxy- 00012 metabolism
uridine
As discussed under Example 6, ADMA, an inhibitor of Nitric oxide
(NO) metabolism, exhibited significant increases in fold changes in
response to exposure of hESCs to strong teratogens. NO has been
identified as a candidate mechanism for neural tube disorders, and
NO is essential for normal axial development (Alexander et al.
2007). Monomethyl-L arginine, a specific inhibitor of NO,
demonstrated NO is so critical for mammalian development, that both
an excess as well as deficiency of NO can be embryotoxic (Lee &
Juchau 2005). The present study is the first time that two human
intermediates in this network, arginine and dimethylarginine (FIG.
5, Table 7) were measured and exhibited statistically significant
changes in response to several known disruptors of human
development.
Other key small molecules changed as reported in the results
section, share the same chemical network, namely GABA and glutamic
acid. GABA is the principal inhibitory neurotransmitter in the
brain. Glutamate dysregulation has the potential to severely
compromise neurogenesis, possibly contributing to cell death in
specific regions of the brain (reviewed in (Bauman 1998)).
Specifically, glutamate is vital for programmed cell death from
development until three years of age. Not only does the metabolite
glutamate regulate neuronal survival or death, but it also plays a
critical role in cognition, learning and memory (Tashiro et al.
2006). Glutamate and GABA are also known modulators of neuronal
migration during development (Lujan et al. 2005); hence concomitant
dysregulation of glutamate and GABA metabolism can provide an
important mechanism for human developmental toxicity.
Surprisingly, other small molecules reported herein, such as
succinic acid, are likely to play synergistic roles with glutamic
acid and GABA in the mechanism of teratogen-induced toxicity, given
that simultaneous changes to rate-limiting enzymes in both networks
(GABA-transaminase and succinic semialdehyde dehydrogenase) are
present in certain neuropsychiatric disorders, such as succinic
semialdehyde dehydrogenase deficiency or GABA aciduria (reviewed in
(Pearl et al. 2007)). Although this syndrome is inherited, in
contrast to the environmental nature of developmental toxicity, it
becomes even more striking that valproate has been shown to
aggravate symptoms in these patients, through further detriment to
GABA and succinic acid metabolism (Shinka et al. 2003), which is a
direct indication of the potential of this hESC-based developmental
toxicity screen to elucidate biologically meaningful mechanisms of
compound toxicity.
The metabolomics results presented here suggest that busulfan
affects GABA levels in the developing embryo, which in turn can
underlie neural developmental disruption. These examples illustrate
how metabolomics unravels mechanistic networks of developmental
toxicity through direct analysis of secreted or excreted
metabolites from hESCs dosed with known teratogens. In doing so, it
is quite possible to model the potential for developmental toxicity
of new drugs screened in preclinical development with a high degree
of predictability while providing information about the mechanisms
of toxicity. Further studies will allow classification of compounds
into subgroups of developmental toxicity such neural developmental
disruptors or those likely to cause structural malformations.
In one embodiment five or more of the validated small molecules
listed in Table 8 are used to predict the teratogenicity of a test
compound according to the methods of the present invention. In
other embodiments, ten or more of the validated small molecules
listed in Table 8 are used to predict the teratogenicity of a test
compound according to the methods of the present invention.
Example 8
Metabolic Networks Involved in Developmental Toxicity
Two experimental systems were deployed per chemical: viability
studies and metabolomics studies. These assays were performed in
two phases. Cell viability assays were performed to establish the
three concentrations to dose hES cells for metabolomic studies.
First, hES cells were dosed with eight concentrations of each then
cell viability measurements were made using the MultiTox-Fluor cell
based assay (Promega). Concentration curves for each chemical were
calculated to determine the three concentrations for the
metabolomics analysis. The final concentrations employed in this
study were those that caused no cell death and minimal cell death,
if possible.
For metabolomic analysis, hES cells were dosed at the three
concentrations for each chemical compound based on the cell
viability data. Media controls (no cells), dosed media controls (no
cells with dosed media), and controls (cells with undosed media)
were also included in the experimental design (FIG. 1). Spent media
was collected following a three day dosing period. The collected
media was immediately quenched in acetonitrile then stored at
-80.degree. C. until later analysis.
In both the viability and metabolomics steps, 96-well plates were
seeded with 250,000 cells/well of WA09 hES cells. These cells were
"dosed" for three days. Each day for three days, the spent media
was removed and replaced with mTeSR9 media containing the
designated compound. Each compound stock solution was made in DMSO
and each final solution used to dose hES cells contained 0.1% DMSO.
Spent media samples were collected on the fourth day and prepared
for metabolomic analysis.
Sample Preparation:
In order to isolate small molecular weight compounds (<10 kDa)
from samples for metabolomics experimentation, the Millipore
Multiscreen Ultracel-10 molecular weight cut off plates were used.
These plates were first washed with a 0.1% sodium hydroxide
solution and then twice with water to remove contaminant polymer
product. The quenched sample were added to the washed filter which
was centrifuged at 2000.times.g for approximately 240 minutes at
4.degree. C., the flowthrough was collected, then dried overnight
in a SpeedVac. The dried samples were reconstituted in 70 .mu.L of
1:1 0.1% formic acid in water:0.1% formic acid in acetonitrile and
transferred to a 96-well plate.
LC-MS Experiment:
Samples were analyzed in both ESI positive and ESI negative modes
on an Agilent QTOF instrument, operated in high resolution,
extended dynamic range mode. Two Phenomenex Luna HILIC columns;
100.times.3 mm; P/N 00D-4449-Y0, S/N 440333-5, and S/N 512570-3
were used for the analysis.
Data Processing:
Sample Naming Scheme
Sample names used for statistical analysis are coded with the
experimental compound name (ST003G.74.A, ST003G.75.B, etc.), the
dose level (High (H), Medium (M), or Low (L)), and repetitions
(a-h). The sample name "ST003G.74.A_H_b" can be decoded as
experimental compound 74A, dose level "high," repetition b and the
sample name "ST003G.84.K_L_b" can be decoded as experimental
compound 84K, dose level "low," repetition b and so on.
Data Processing
mzData File Creation
Agilent raw data files were converted to the open source mzData
file format using Agilent MassHunter Qual software version 3.0.
During the conversion process, deisotoping (+1 charge state only)
was performed on the centroid data and peaks with an absolute
height less than 400 (approximately double the typical average
instrument background level). The resulting mzData files contain
centroid data of deisotoped (+1 charge state only) peaks that have
an absolute height greater than 400 counts.
Mass Feature Creation and Integration.
Peak picking and feature creation were performed using the open
source software library XCMS. Mass features (peaks) were detected
using the centwave algorithm. Following peak picking deviations in
retention times were corrected using the obiwarp algorithm that is
based on a non-linear clustering approach to align LC-MS samples.
Mass feature bins or groups were generated using a density based
grouping algorithm. After the data had been grouped into mass
features, missing features were integrated based on retention time
and mass range of a feature bin using the iterative peak filling.
Feature intensity is based on the Mexican hat integration values of
the feature extracted ion chromatograms.
Solvent/Extraction Blank Filter
The extraction blank filter removes ions associated with the sample
extraction process and background ions present in the LC-MS system.
Features were removed from the metabolomics dataset if the average
in the experimental samples was less than five times the average
abundance in the extraction blanks.
Contamination DB Filter
The contamination DB filter removes features with a mass match
within 20 ppm to entries in Stemina's proprietary database which
contains a number of contaminants such as plasticizers and PEG
compounds identified in previous studies. Features are removed
without respect to retention time if they match a contaminant or a
common charge specific adduct of a contaminant.
PCA Based Outlier Removal
Sample outlier detection and removal is performed on the log based
2 transformed pareto scaled abundance values by experimental factor
use NIPALS based PCA. A distance measurement is used to flag and
remove outlier LC-MS samples that are outside the 0.975 quantile of
the distance measurements.
Abundance and Reproducibility Filter
Prior to statistical analysis, features were filtered by factor
(e.g. experimental compound by dose) to remove features that did
not exhibit abundance greater than 12,500 (ESI negative mode) or
50,000 (ESI positive mode) in 66% of the LC-MS runs for at least
one dose level (L, M, H) of at least one experimental compound
(e.g., ST003G.82.I). This filter selects against spurious low
abundance features at the level of detection that are not
reproducibly measured, and features that may not have peak shapes
amenable to reproducible detection and/or integration. This filter
typically removes a large portion of the metabolomics dataset, and
focuses the analysis on the most reliable and valuable features.
For example a feature with abundance values greater than 12,500 in
70% of the negative mode LC-MS samples in one dose level of one
experimental compound and abundance values greater than 12,500 in
none of the other experimental compound by dose combinations would
pass the filter because at least one experimental compound by dose
factor satisfies the filter criteria.
Data Transformation and Normalization.
All data were log base two transformed. Normalization for each
factor level was performed by subtracting the column (sample) mean
and dividing by the row (feature) standard deviation for each value
(autoscaling).
Differential Analysis of Mass Features (Univariate)
Mass features were evaluated under the null hypothesis that no
difference is present between the means of experimental classes and
the alternative hypothesis that there is a difference between
experimental classes. Welch two sample T-tests were performed as a
parametric method that does not assume equal variances of the
experimental classes. A one-way ANOVA was performed on each
experimental compound to evaluate the difference in means across
the three dose levels. Tukeys post hoc tests were performed to
identify significant differences between the dose levels. Following
statistical analysis false discovery rates were controlled for
multiple testing using the Benjamini-Hochberg (1995) method of p
value correction of the ANOVA and Welch T-tests.
Analysis of Mass Features (Multivariate)
Annotation of mass features was carried out by comparing the m/z
mass values of the mass features to Stemina's internal metabolite
database containing records from multiple public databases such as
HMDB, KEGG, PubChem Compound, and METLIN and company-specific
metabolite data. The features were annotated with respect to the
appropriate adducts for each ESI mode. The identities of all mass
features were not validated and therefore all annotations are
putative.
Identification of Mass Features
Annotation of mass features was carried out by comparing the m/z
mass values of the mass features to Stemina's internal metabolite
database containing records from multiple public databases such as
HMDB, KEGG, PubChem Compound, and METLIN and company-specific
metabolite data. The features were annotated with respect to the
appropriate adducts for each ESI mode. The identities of all mass
features were not validated and therefore all annotations are
putative.
Networks Analysis
Pathways enrichment analysis was performed by mapping annotated
mass features for each experimental compound to human metabolic
networks using KEGG compound ids. Hypergeometric p-values and false
discovery rates (FDR) were used to assign a quantitative measure of
statistical significance to each network. Features derived from ESI
negative and positive mode for each experimental compound were
pooled for this analysis. False positive results can be generated
by isobaric compounds that generate multiple "hits" in a network
from the same mass, so unique masses instead of unique compound ids
were used for these calculations. The relevant parameters used to
calculate hypergeometric p-values for each network were: the number
of unique mass "hits", the number of unique masses in the network,
and the total number of unique masses in all of the human networks
in the KEGG database. For each experimental compound, the p-values
for the derived networks were converted to FDR using the Benjamini
and Hochberg (1995) correction.
Selection of Interesting Features
Feature Selection was performed on a per compound basis using a
one-way anova evaluating the difference of dose level means and on
a per dose basis using Welch T-tests and PLS-DA VIP score. Features
were selected for further evaluation if they had a Welch
FDR<0.05 or a PLS-DA VIP score >20 with at least a 50% fold
change and control cells showed at least a 40% difference to
control media (secreted, consumed, or identified), or Anova FDR
<0.05 and a difference between 0.1.times. and 10.times. dose was
at least 50%. If a feature was selected as interesting in a drug or
dose level comparison it was then evaluated experiment wide for
fold changes. Following feature selection only significant features
putatively annotated as mammalian in origin and present on KEGG
network diagrams were further evaluated. Pathway enrich analysis
was then performed on the selected features and features in
networks exhibiting a statistically significant enrichment were
further evaluated for fold changes. These selection criteria
focused the analysis on biochemical pathways.
Results and Discussion:
Metabolomic analysis of the cell culture supernatant extracts
resulted in a set of 324 features in ESI positive mode and 307
features in ESI negative mode after selection for statistical
significance and putative mammalian annotations. Following
selection, features were passed through a quality control
evaluation of extracted ion chromatograms (EICs) to confirm the
validity of individual mass features. Features passing quality
control were further evaluated to confirm estimated fold changes.
After removing poor quality and duplicate features, the remaining
ESI positive and ESI negative mode features were combined into a
unified dataset for evaluation of pathway enrichment by treatment.
These mass features mapped to 86 different KEGG networks of which
15 exhibited a statistically significant (FDR <0.1) enrichment
of annotated features in at least one treatment (Table 9). EICs for
all metabolites in 4 networks that exhibited the most significant
enrichment were plotted and feature quality and fold changes were
evaluated.
Changes in metabolites associated with the urea cycle, glutamate
metabolism, and the citric acid cycle have been associated with
exposure of hES cells to teratogens. Several of the annotated mass
features were evaluated for changes in at least two dose levels
(unless otherwise noted) of the blinded compounds. Succinic acid
(TCA cycle) is generally decreased in hES cells treated with
teratogens and unchanged in non-teratogens. In this study, succinic
acid was decreased in at least two dose levels in cells treated
with ST003G.74.A, ST003G.75.B, ST003G.76.C, ST003G.77.D,
ST003G.80.G, ST003G.81.H. Treatment with teratogens leads to a
decrease in accumulation of dimethylarginine (DMA, urea cycle)
usually observed in combination with increases in arginine
(arginine and proline metabolism) secreted by hES cells. In the
current study, blinded compounds exhibited increased secretion of
DMA in ST003G.82.I, ST003G.83.J, ST003G.84.K and ST003G.85.L, a
mixed response in ST003G.77.D and ST003G.78.E, and decreased
accumulation in ST003G.80.G while arginine was not significantly
changed in this study. Glutamic Acid (glutamate metabolism)
exhibited increased secretion in ST003G.74.A and ST003G.84.K, a
mixed response in ST003G.78 E and ST003G.80.G following treatment
while hES cells following treatment with teratogens show a pattern
of either increased or decreased levels of glutamic acid.
.gamma.-Aminobutyric acid (GABA, neuroactive ligand-receptor) which
can be increased in hES after treatment with teratogens was
increased in ST003G.84.K and decreased in ST003G.75.B. Aspartic
acid (urea cycle, glutamate metabolism) is generally increased in
the media of hES cells following treatment with teratogens was
decreased in ST003G.74.A and ST003G.75.B and increased in
ST003G.77.D and ST003G.80.G. Malic acid, which is generally changed
in teratogens in a more extreme manner than non-teratogens
exhibited extreme fold changes in the high dose levels of
ST003G.78.E, ST003G.79.F, ST003G.80.G, ST003G.82.I, and
ST003G.85.L.
TABLE-US-00009 TABLE 9 Summary of pathway enrichment analysis
performed on positive and negative features. Pathway Description
74.A 75.B 76.C 77.D 78.E 79.F 80.G 81.H 82.I 83.J 84.K- 85.L
Alanine, aspartate 0 0 0 0 1 1 4 0 7 0 0 1 and glutamate metabolism
Arginine and proline 1 0 2 3 2 0 6 0 15 0 1 1 metabolism Ascorbate
and 0 0 1 4 1 0 2 0 11 5 0 2 aldarate metabolism Citrate cycle (TCA
0 0 0 1 2 0 2 0 5 0 0 1 cycle) Cysteine and 0 0 0 0 0 0 4 0 8 0 0 2
methionine metabolism Galactose 0 0 0 6 0 0 0 0 13 9 0 2 metabolism
Glutathione 0 0 1 3 1 1 4 0 3 0 0 1 metabolism Glyoxylate and 0 0 0
1 3 0 2 1 6 0 0 3 dicarboxylate metabolism Nicotinate and 0 0 1 0 5
0 1 0 6 0 0 5 nicotinamide metabolism Pantothenate and 0 0 1 1 3 0
1 0 5 0 0 3 CoA biosynthesis Pentose and 0 0 0 0 1 1 1 0 13 1 0 3
glucuronate interconversions Pentose phosphate 0 0 0 3 0 1 1 0 6 4
0 1 pathway Propanoate 0 0 1 0 1 0 7 0 5 0 0 3 metabolism Pyruvate
metabolism 0 0 0 1 1 0 1 0 8 0 0 6 Vitamin B6 0 0 0 0 0 1 2 0 6 1 0
3 metabolism The values indicate the number of unique KEGG ID
annotations identified across dose levels for each drug. Cells
highlighted grey indicate a statistically significant enrichment
(FDR < 0.1) in at least one treatment dose level.
Example 9
Prediction of Teratogenicity of Test Compounds
The potential teratogenicity of the individual compounds analyzed
in Example 8 were further validated.
Data Analysis and Results:
Prediction of teratogenicity was performed using a partial least
squares discriminate analysis (PLS-DA) model based on metabolic
changes observed in the spent cell culture media (secretome) from
WA09 human embryonic stem (hES) cells treated with pharmaceutical
agents. The PLS-DA classifier model was trained on data previously
acquired in the DevTox project for the secretome of hES cells that
had been treated with therapeutic circulating doses of 22
pharmaceutical agents of known teratogenicity (Table 11). These
included 11 known teratogens and 11 known non-teratogens. The
current model is based on the mean fold change (treatment versus
its associated intra-experimental control) of 15 metabolites common
among the secretome of hES cells treated with pharmaceutical agents
and unknown chemical compounds. The results of this model for the
DevTox drugs are shown in Table 11. For this study of EPA
compounds, the experiment represents the first instance of this
PLS-DA model as applied to the prediction of non-pharmaceutical
environmental toxicants.
TABLE-US-00010 TABLE 10 Features utilized in the PLS-DA prediction
of Teratogenicity. Metabolites in bold font indicate a previously
validated metabolite. Annotation m/z RT Polarity
methylsulfonylacetonitrile 120.0116 618 ESI(+) Aspartic Acid
134.0460 431 ESI(+) N*-Acetylspermidine 188.1760 431 ESI(+)
Dimethyl-L-arginine 203.1504 445 ESI(+) Unknown 215.1387 466 ESI(+)
L-Cystathionine 223.0750 593 ESI(+) Unknown 234.8904 246 ESI(+)
Unknown 251.0666 105 ESI(+) Unknown 403.0839 653 ESI(+) GABA
102.0561 467 ESI(-) Fumaric acid 115.0057 111 ESI(-) Valine
116.0712 309 ESI(-) Succinic acid 117.0190 82 ESI(-) Aspartic acid
132.0299 472 ESI(-) Pantoic acid 147.0658 81 ESI(-)
TABLE-US-00011 TABLE 11 Prediction of teratogencity by
PLS-DA-DevTox pharmaceutical compounds that were utilized in the
PLS-DA Model and their resulting predictions. The high (H = 10x)
and low (L = 0.1x) dose treatments of the pharmaceutical agents
utilized in the training set are included as a reference (Note: M =
1x, corresponds to the circulating dose. This dose was used in the
training of the PLS-DA model and hence omitted from prediction
table). Bold font indicates non-teratogen at circulating dose,
regular font indicates teratogen at circulating dose. Drug
Treatment Prediction % Non % Ter Confidence 5-Fluorouracil_H Ter
0.32 0.68 0.36 5-Fluorouracil_L Ter 0.28 0.72 0.44 Accutane_H Ter
0.3 0.7 0.4 Accutane_L Ter 0.33 0.67 0.34 Busulfan_H Ter 0.28 0.72
0.44 Busulfan_L Ter 0.29 0.71 0.42 Carbamazepine_H Ter 0.37 0.63
0.26 Carbamazepine_L Non 0.5 0.5 0 Cyclophosphamide_H Ter 0.45 0.55
0.1 Cyclophosphamide_L Ter 0.41 0.59 0.18 CytosineArabinoside_H Ter
0.36 0.64 0.28 CytosineArabinoside_L Ter 0.33 0.67 0.34
Hydroxyurea_H Ter 0.32 0.68 0.36 Hydroxyurea_L Non 0.64 0.36 0.28
Methotrexate_H Ter 0.42 0.58 0.16 Methotrexate_L Ter 0.48 0.52 0.04
RetinoicAcid_H Ter 0.3 0.7 0.4 RetinoicAcid_L Ter 0.3 0.7 0.4
Rifampicin_H Ter 0.27 0.73 0.46 Rifampicin_L Ter 0.46 0.54 0.08
Thalidomide_H Ter 0.3 0.7 0.4 Thalidomide_L Non 0.65 0.35 0.3 VPA_H
Ter 0.34 0.66 0.32 VPA_L Ter 0.43 0.57 0.14 Ascorbic Acid_H Non
0.57 0.43 0.14 Ascorbic Acid_L Non 0.57 0.43 0.14 Caffeine_H Non
0.53 0.47 0.06 Caffeine_L Non 0.58 0.42 0.16 Diphenhydramine_H Non
0.73 0.27 0.46 Diphenhydramine_L Non 0.76 0.24 0.52 Doxylamine_H
Ter 0.38 0.62 0.24 Doxylamine_L Non 0.58 0.42 0.16 Folic Acid_H Non
0.59 0.41 0.18 Folic Acid_L Non 0.59 0.41 0.18 Isoniazid_H Non 0.59
0.41 0.18 Isoniazid_L Non 0.76 0.24 0.52 Levothyroxine_H Non 0.59
0.41 0.18 Levothyroxine_L Non 0.69 0.31 0.38 PenicillinG_H Non 0.57
0.43 0.14 PenicillinG_L Non 0.55 0.45 0.1 Retinol_H Non 0.65 0.35
0.3 Retinol_L Non 0.75 0.25 0.5 Saccharin_H Non 0.8 0.2 0.6
Saccharin_L Non 0.75 0.25 0.5 Thiamine_H Non 0.78 0.22 0.56
Thiamine_L Non 0.82 0.18 0.64
TABLE-US-00012 TABLE 12 Prediction of teratogencity by PLS-DA for
EPA compounds. % Non and % Ter are the PLS-DA generated class
probabilities. Confidence is the difference between class
probabilities. Confidence values less than 0.1 are considered
inconclusive with respect to the class prediction. Treatment
Prediction % Non % Ter Confidence ST003G.74.A_H Ter 0.35 0.65 0.3
ST003G.74.A_M Ter 0.44 0.56 0.12 ST003G.74.A_L Non 0.59 0.41 0.18
ST003G.75.B_H Ter 0.49 0.51 0.02 ST003G.75.B_M Non 0.65 0.35 0.3
ST003G.75.B_L Ter 0.4 0.6 0.2 ST003G.76.C_H Ter 0.42 0.58 0.16
ST003G.76.C_M Non 0.64 0.36 0.28 ST003G.76.C_L Non 0.75 0.25 0.5
ST003G.77.D_H Ter 0.39 0.61 0.22 ST003G.77.D_M Ter 0.38 0.62 0.24
ST003G.77.D_L Ter 0.37 0.63 0.26 ST003G.78.E_H Non 0.69 0.31 0.38
ST003G.78.E_M Non 0.64 0.36 0.28 ST003G.78.E_L Non 0.59 0.41 0.18
ST003G.79.F_H Ter 0.37 0.63 0.26 ST003G.79.F_M Non 0.51 0.49 0.02
ST003G.79.F_L Ter 0.45 0.55 0.1 ST003G.80.G_H Non 0.59 0.41 0.18
ST003G.80.G_M Non 0.63 0.37 0.26 ST003G.80.G_L Non 0.66 0.34 0.32
ST003G.81.H_H Non 0.67 0.33 0.34 ST003G.81.H_M Non 0.69 0.31 0.38
ST003G.81.H_L Non 0.75 0.25 0.5 ST003G.82.I_H Ter 0.3 0.7 0.4
ST003G.82.I_M Ter 0.42 0.58 0.16 ST003G.82.I_L Non 0.57 0.43 0.14
ST003G.83.J_H Non 0.73 0.27 0.46 ST003G.83.J_M Non 0.75 0.25 0.5
ST003G.83.J_L Non 0.75 0.25 0.5 ST003G.84.K_H Non 0.73 0.27 0.46
ST003G.84.K_M Non 0.79 0.21 0.58 ST003G.84.K_L Non 0.81 0.19 0.62
ST003G.85.L_H Ter 0.45 0.55 0.1 ST003G.85.L_M Ter 0.44 0.56 0.12
ST003G.85.L_L Ter 0.41 0.59 0.18
Conclusions:
The prediction model that has been developed classifies the
EPA-provided chemical agents ST003G.74.A, ST003G.75.B, ST003G.77.D,
ST003G.82.I, ST003G.85.L as potential teratogens, and the chemical
agents ST003G.76.C, ST003G.78.E, ST003G.80.G, ST003G.81.H,
ST003G.83.J, ST003G.84.K as potential non-teratogens. The chemical
agent ST003G.76.0 is predicted as a teratogen only at the highest
dose level. See Table 12.
Doxylamine was added to the test set as a reference pharmaceutical
treatment (ST003G-85-L). Doxylamine has been ranked by the FDA as a
pregnancy category B drug, which means that animal studies show no
risk of that particular drug inducing birth defects and there are
no studies in pregnant women. This compound was analyzed in the
developmental toxicity assay. At the low and medium dose,
Doxylamine was classified as a non-teratogen, while at the high
concentration; it was classified as a teratogen (Table 11). In
these studies all three concentrations (low, medium, and high) of
Doxylamine was classified as being a teratogen. The concentrations
of Doxylamine used in these studies and the corresponding
teratogenicities assigned at each concentration are shown in the
table below. There appears to be a critical concentration which
causes the classification of Doxylamine to switch from a
non-teratogen to a teratogen and, according to our data, it is
between 0.38 and 1 .mu.M.
TABLE-US-00013 TABLE 13 Doxylamine dose levels and PLS-DA
teratogenocity predictions. [Doxylamine] Teratogenicity (.mu.m)
Project Classification 0.038 devTox low Non 0.38 devTox medium Non
1 EPA low Ter 3.8 devTox high Ter 10 EPA medium Ter 100 EPA high
Ter
To ensure the teratogenicity classifications are not merely a
reflection of cell viability, the cell viability data was analyzed
(FIG. 23). As indicated below, there is no correlation between
teratogenicity classification and cell viability, and at 1 .mu.M
Doxylamine the cells are actually thriving (FIGS. 23, a to c).
There is some cell death at 0.38 .mu.M, however, at this
concentration, Doxylamine was still not classified as a teratogen.
This example of the prediction on the teratogenicity of Doxylamine
helps substantiate the present model of teratogenicity.
Example 10
Network Interpretation
Several biochemical pathways with a statistically significant
enrichment of annotated mass features were further evaluated. Of
most interest in the present findings are nicotinate and
nicotinamide metabolism, pantothenate and CoA biosynthesis,
glutathione metabolism, and arginine and proline metabolic
networks. These pathways were examined to elucidate connections
between these pathways and birth defects. Metabolites within the
pathways which are marked with a black circle are those with unique
masses while those which are marked with a grey circle are isobaric
and may be another metabolite with the same molecular weight.
Nicotinate and Nicotinamide Metabolic Network:
Nicotinate and nicotinamide are precursors of the coenzymes
nicotinamide-adenine dinucleotide (NAD+) and nicotinamide-adenine
dinucleotide phosphate (NADP+), which, when reduced, are important
cofactors in many redox reactions. When nicotinic acid is
deficient, pellagra can result. It was found that mutations in the
nicotinamide N-methyl transferase (NNMT) could lead to risk of
spina bifida (Lu et al., Mol. Teratology, 82:670-675, 2008) and it
is possible that alterations to this pathway could lead to birth
defects and thus, measurements of fold change of metabolites in
this pathway could indicate a compound's teratogenicity
Pantothenate and CoA Biosynthesis Network:
A significant number of putative metabolite annotations from the
pantothenate and CoA biosynthesis network exhibited statistically
significant changes across a number of compounds. The network
figure for the Pantothenate and CoA biosynthesis network shows the
putative annotations, marked with either a black circles, or a grey
circle (those metabolites highlighted that are grey circles are
isobaric while those that are black circles have unique
masses.)
The pantothenate and CoA biosynthesis network produces CoA which
attaches to a long-chain fatty acid to eventually form acetyl-CoA
which enters the TCA cycle resulting in ATP synthesis. Thus
aberrations to this network can result in energy production
abnormalities, which can, in turn, cause severe impairment of
cellular processes. Of most importance in the network is the
pantothenate availability, as the phosphorylation of this
metabolite is the rate-limiting step of CoA production and it has
been observed that impaired energy result along with neurological
symptoms (Rock et al., J. Biol. Chem., 275:1377-1383, 2000) as a
result of low levels of pantothenate. Furthermore, it was found
that maternal pantothenate deficiency results in a teratogenic
effect on the fetus (Nelson et al., J. Nutr., 62:395-405, 1957;
Baker et al., Am. J. Clin. Nutr., 28:56-65, 1975). Given these
associations of alterations to the pantothenate network and birth
defects, it is plausible to correlate chemicals which cause
abundance changes of metabolites within the pantothenate network
with the likelihood that particular chemicals causing these changes
may in turn have the ability to disrupt human development, and
possibly induce birth defects.
Glutathione Network:
The glutathionine network plays a role in oxidative stress.
Glutathione, an essential metabolite of the network, can exist in a
reduced or oxidized state. In its reduced state, glutathione has
the ability to protonate free radicals and, thus, acts as an
antioxidant. Oxidative stress is associated with neurodegenerative
disease (Simonian et al., Ann Rev Pharm. Tox., 36:83-106, 1996),
pulmonary disease (Repine et al., Am. J. Resp. Critical Care Med.,
156:341-357, 1997), and has even been related to preeclampsia
(Walsh et al., Semin. Reprod. Med., 16:93-104, 1998). There have
been several studies which relate glutathione levels with birth
defects. For example, Isibashi et al. had found that glutathione
depletion and oxidative stress strongly implicate birth defects in
animals (Isibashi et al., Free Rad. Biol. Med., 22:447-454, 1997).
Zhao et al. also found such a relationship in humans and discovered
that women with neural tube defect pregnancies had higher levels of
oxidized glutathione than the control group (Zhao et al., Birth
Defects Research Part A: Clinical and Molecular Teratology,
76:230-236, 2006). Due to this association of the glutathione
network and birth defects, it is possible to further study the fold
changes for the metabolites within this network in order to
classify each chemical compound as a potential teratogen or
not.
Arginine and Proline Metabolic Network:
Several statistically significantly altered small molecules within
the arginine and proline metabolic network were found. Most
interesting is the presence of dimethylarginine, arginine, and
citrulline. Nitric oxide synthase converts L-Arginine to
L-Citrulline. Dimethylarginine is an inhibitor of Nitric Oxide
Synthase. Studies have found that nitric oxide synthase is
essential for neural tube closure (Nachmany et al., J. Neurochem.,
96:247-253, 2006) and so modifications to this reaction and to
levels of L-citrulline and L-arginine could indicate a chemical
compound's ability to induce birth defects.
All references cited herein are incorporated by reference. In
addition, the invention is not intended to be limited to the
disclosed embodiments of the invention. It should be understood
that the foregoing disclosure emphasizes certain specific
embodiments of the invention and that all modifications or
alternatives equivalent thereto are within the spirit and scope of
the invention as set forth in the appended claims.
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